System, method and non-transitory computer-readable medium for cryptocurrency mining

ABSTRACT

A computer may be provided on a mining machine comprising a mother board, a power supply in operable communication with the mother board, an input/output interface in communication with the mother board, and a plurality of hash boards each in communication with the mother board and comprising a plurality of mining chips. The computer may execute instructions that cause the computer to perform establishing communication with an external device, retrieving at least one profit variable from the external device, calculating an estimated profitability of a first mining chip based on the profit variable, and adjusting a chip voltage supplied to the first mining chip and adjusting a chip frequency of the first mining chip to maximize the estimated profitability. Alternatively, the instructions may cause the computer to adjust the chip voltage and the chip frequency to maintain a temperature within a predetermined range.

CROSS REFERENCE TO RELATED APPLICATION

This Application claims the benefit of U.S. Provisional PatentApplication No. 63/229,685 filed Aug. 5, 2021, the entire contents ofwhich are incorporated herein by reference.

BACKGROUND OF THE DISCLOSURE

Managing the efficient operation of cryptocurrency mining machines isimportant for reducing operating costs, and improving profits.Cryptocurrency mining machines require large amounts of processing powerused by mining chips for solving complex mathematical computations whenmining digital currency. Because an increase in power consumptionresults in higher operating costs, mining digital currency can be anexpensive endeavor. Mining chips, such as, application-specificinterface chips (ASIC), or field programmable gate array chips (FPGA),are embedded with specific mining algorithms tailored for miningdifferent types of digital coins. For example, ASIC chips employ SHA-266algorithms for mining bitcoins. The speed at which these algorithmssolve mathematical equations, or the amount of calculations performedper second, is defined by hashrate. As hashrate increases, so does thespeed of mining digital coins which correlates to higher profits.

Various techniques have been implemented to overcome challengesassociated with lowering costs while improving operation efficiency ofmining machines. In an effort to increase hashrate to garner higherprofits, the operating frequency applied to the mining chips is oftenoverclocked to increase the hash rate. However, adjusting the frequencyalone of mining chips generally increases hashing power which in turncompromises the operating efficiency of the mining machines. Further,overclocking mining chips, and increasing hashing power, often producesexcessive heat that, and if not managed properly, can damage miningchips, and seriously affect the operating efficiency. Althoughconventional methods of cooling chips have been employed to bettermanage the heat generated, controlling frequency alone in an effort tomanage operating costs and improve profits provides on-going challenges,and limited benefits.

Although prior art systems have employed various strategies foradjusting the operating frequency of mining chips, the operating voltageapplied to mining chips, and provided by the power supply of the miningmachine, has remained fixed. Maintaining fixed voltages on mining chipsnot only wastes power, and increases heat, but effects the hashing powerand efficiency of mining machines as well. The prior art fails toaddress the need for dynamically adjusting both the operating voltage,and frequency of mining chips to manage power usage, and hashrate ofmining chips based on the measured temperature, in real time, of miningchips. Also, conventional mining systems also adjust the operatingfrequency of mining chips when mining different types of digitalcurrency to find which digital currency provides the highest profit.However, the prior art fails to address the need for dynamicallyadjusting both the operating voltage, and operating frequency of miningchips based on profits to find the highest profit when mining a single,type of digital currency.

In accordance with the aforementioned problems provided in the priorart, there is a need for a system, and method for auto-tuningcryptocurrency mining machines by dynamically adjusting both theoperating voltage, and operating frequency of ASIC chips based onvarious condition parameters including temperature, to manage powerusage, and hashrate of ASIC mining chips, and applying tuned parametersthat provide the highest profit for a single type of digital currency toefficiently and effectively operate cryptocurrency mining machines.There is also a need for a non-transitory computer-readable storagemedium that includes a dynamic tuning firmware that is user-friendly,and remotely accessible by users for preconfiguring various operatingparameters, and profit variables, and for controlling and managingauto-tuning of the cryptocurrency mining machines.

BRIEF DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

An exemplary embodiment of a non-transitory computer-readable medium maystore computer-executable instructions to be executed by a computer on amining machine including a mother board, a power supply in operablecommunication with the mother board, an input/output interface inoperable communication with the mother board, and a plurality of hashboards each in operable communication with the mother board andincluding a plurality of mining chips. When executed by the computer,the computer-executable instructions may cause the computer to performestablishing communication with an external device via an externalnetwork, retrieving at least one profit variable from the externaldevice via the external network, calculating an estimated profitabilityof a first mining chip of the plurality of mining chips based on theprofit variable, and adjusting a chip voltage supplied to the firstmining chip and adjusting a chip frequency of the first mining chip tomaximize the estimated profitability.

An exemplary embodiment of a non-transitory computer-readable medium maystore computer-executable instructions to be executed by a computer on amining machine including a mother board, a power supply in operablecommunication with the mother board, an input/output interface inoperable communication with the mother board, and a plurality of hashboards each in operable communication with the mother board andincluding a plurality of mining chips. When executed by the computer,the computer-executable instructions may cause the computer to performcause the computer to perform measuring a temperature of a first miningchip of the plurality of mining chips or a first hash board of theplurality of hash boards using a temperature sensor, and adjusting achip voltage supplied to the first mining chip and adjusting a chipfrequency of the first mining chip based on maintaining the temperaturewithin a predetermined temperature range.

An exemplary embodiment of a method for cryptocurrency mining mayinclude providing a mining device including a mother board, a powersupply in operable communication with the mother board, an input/outputinterface in operable communication with the mother board, and aplurality of hash boards each including a plurality of mining chips. Theplurality of hash boards may be in operable communication with themother board. The method may further include establishing communicationwith the mining device via an external network, establishingcommunication between the mining device and an external device via theexternal network, retrieving a profit variable from the external devicevia the external network, calculating an estimated profitability of afirst mining chip of the plurality of mining chips based on the profitvariable, and adjusting a chip voltage supplied to the first mining chipand adjusting a chip frequency of the first mining chip to maximize theestimated profitability.

An exemplary embodiment of a system for cryptocurrency mining mayinclude a mining device including a mother board, a power supply inoperable communication with the mother board, an input/output interfacein operable communication with the mother board, and a plurality of hashboards each including a mining chip. The plurality of hash boards may bein operable communication with the mother board. The system may furtherinclude a dynamic tuning firmware in operable communication with themother board. The dynamic tuning firmware may be configured to establishcommunication with an external device via an external network, retrievea profit variable from the external device via the external network,calculate an estimated profitability of a first mining chip of theplurality of mining chips based on the profit variable, adjust a chipvoltage supplied to the first mining chip and adjust a chip frequency ofthe first mining chip to maximize the estimated profitability. Theplurality of mining chips may include an application-specific integratedcircuit.

BRIEF DESCRIPTION OF THE DRAWINGS

A more particular description will be rendered by reference to specificembodiments thereof that are illustrated in the appended drawings.Understanding that these drawings depict only typical embodimentsthereof and are not therefore to be considered to be limiting of itsscope, exemplary embodiments will be described and explained withadditional specificity and detail through the use of the accompanyingdrawings in which:

FIG. 1 illustrates a schematic representation of a system, method, andnon-transitory computer-readable storage medium for auto-tuningcryptocurrency mining machines based on condition parameters showingcomputing devices in electrical communication with a plurality ofcryptocurrency mining machines via, an electrical communication network,in accordance with one exemplary embodiment;

FIG. 2 illustrates a schematic block diagram of a computing device forelectrically communicating with, managing, and controlling one or morecryptocurrency mining machines via, the communication network;

FIG. 3 illustrates a side view of a cryptocurrency mining machineshowing a portion of a housing removed, to illustrate a schematicrepresentation of functional components including a power supply, amother board, I/O interface, hash boards including ASIC chips, coolingmodules in electrical communication with a fan control module, anddynamic tuning firmware provided in a computer-readable storage medium,or non-transitory computer-readable storage medium hosted on eachcryptocurrency mining machine;

FIG. 4 illustrates a block diagram of the dynamic tuning firmwareshowing a tuning preset configuration block in functional communicationwith a tuning process block that operate in unison for auto-tuningcryptocurrency mining machines;

FIGS. 5 and 6 illustrate a miner profile configuration screen formanaging operational parameters of a designated mining machine,including a drop down menu for selecting various profile configurations,presetting target chip temperature values, and operating speed ofcooling modules, and a selective operator for enabling an auto-switchmode for auto-tuning the designated cryptocurrency mining machine;

FIG. 7 illustrates a configuration multiplier screen for managingoperational parameters of a plurality of designated mining machines,including a drop down menu for selecting various profile configurations,presetting target chip temperature values, and operating speed ofcooling modules, and a selective operator for enabling an auto-switchmode for auto-tuning the plurality of designated cryptocurrency miningmachines simultaneously;

FIG. 8 illustrates a tuner configuration screen for managinguser-defined chip profile configurations for auto-tuning a designatedcryptocurrency mining machine;

FIG. 9 illustrates a profitability configuration screen forpreconfiguring profit variables for calculating profit for auto-tuningcryptocurrency mining machines based on the calculated profit whenmining a specific, type of digital currency; and

FIG. 10 illustrates a table showing performance values including targetchip frequency, target chip voltage, power usage, hashrate, andefficiency of a designated cryptocurrency mining machine.

Various features, aspects, and advantages of the exemplary embodimentswill become more apparent from the following detailed description, alongwith the accompanying drawings in which like numerals represent likecomponents throughout the figures and detailed description. The variousdescribed features are not necessarily drawn to scale in the drawingsbut are drawn to aid in understanding the features of the exemplaryembodiments.

The headings used herein are for organizational purposes only and arenot meant to limit the scope of the disclosure or the claims. Tofacilitate understanding, reference numerals have been used, wherepossible, to designate like elements common to the figures.

DETAILED DESCRIPTION

Reference will now be made in detail to various exemplary embodiments.Each example is provided by way of explanation and is not meant as alimitation and does not constitute a definition of all possibleembodiments. It is understood that reference to a particular “exemplaryembodiment” of, e.g., a structure, assembly, component, configuration,method, etc. includes exemplary embodiments of, e.g., the associatedfeatures, subcomponents, method steps, etc. forming a part of the“exemplary embodiment”.

For purposes of this disclosure, the phrases “devices,” “systems,” and“methods” may be used either individually or in any combinationreferring without limitation to disclosed components, grouping,arrangements, steps, functions, or processes.

The following detailed description is merely exemplary in nature and isnot intended to limit the described embodiments or the application anduses of the described embodiments. As used herein, the word “exemplary”or “illustrative” means “serving as an example, instance, orillustration”. Any implementation described herein as “exemplary” or“illustrative” is not necessarily to be construed as preferred oradvantageous over other implementations. All of the implementationsdescribed below are exemplary implementations provided to enable personsskilled in the art to make or use the embodiments of the disclosure andare not intended to limit the scope of the disclosure, which is definedby the claims. Furthermore, there is no intention to be bound by anyexpressed or implied theory presented in the preceding technical field,background, brief summary or the following detailed description. It isunderstood that the specific devices and processes illustrated in theattached drawings, and described in the following specification, aresimply exemplary embodiments of the inventive concepts defined in theappended claims. Hence, specific dimensions and other physicalcharacteristics relating to the embodiments disclosed herein are notlimiting, unless the claims expressly state otherwise.

An exemplary embodiment relates to cryptocurrency mining systems andmachines, and more particularly, to a system, method, and non-transitorycomputer-readable storage medium for auto-tuning cryptocurrency miningmachines based on condition parameters including temperature, and miningprofit.

The term, “cryptocurrency”, or “digital currency”, as used herein refersto digital or virtual currency such as digital coins, including but notlimited to, Bitcoin, Litecoin, Dogecoin, Ethereum, Ripple, Omni,Stellar, NEO, Cardano, and alternative coins.

The term, “tuned parameters”, as used herein refers to a target chipvoltage, and target chip frequency, that when applied to each ASIC chipof each cryptocurrency mining machine, allows the ASIC chips to operateat a lowest power consumption defined as the lowest chip voltage valueneeded to overcome ASIC chip instability, and operate at the highesthashrate defined as a chip frequency value that is equal to, or greaterthan, a hashrate threshold of an ideal hashrate of each ASIC chip.

Referring now to the figures wherein like elements are represented bylike numerals throughout, there is shown in FIG. 1 , a schematicrepresentation of a system and method for auto-tuning cryptocurrencymining machines 10 based on various condition parameters includingtemperature and profits, in accordance with one exemplary embodiment.One or more computing devices 12, 14 are used to manage, and control theoperation of cryptocurrency mining mining machines 16, 18, 20, nth overan electrical communication network 22. Each computing device 12, 14interacts with dynamic tuning firmware, hosted on each cryptocurrencymining machine 16, 18, 20, nth, that includes instructions, programs,and/or computer code stored in a computer readable medium to control tomanage the efficient operation of the mining machines, and other aspectsand methodologies of the present disclosure. Miner management softwarecan be hosted on each computing device 12, 14 for interfacing andcommunicating with, and for managing, the dynamic tuning firmware hostedon each cryptocurrency mining machine. In one alternative embodiment,the miner management software can be hosted on a cloud-based system thatis maintained by a third party. A mobile application may be provided foruse on smartphones to communicate directly with the dynamic tuningsoftware, or with the miner management software hosted on a cloud-basedsystem, or on each computing device. The miner management software mayinclude all necessary web-based tools and protocols for interfacing withusers, and the dynamic tuning firmware. It is appreciated that thenumber of computing devices is provided for exemplary purposes only andadditional devices may be used. In one embodiment, there is provided adatabase 24 for hosting, storing, and managing information,instructions, code, look-up tables, data files, applications, machinelearning models/algorithms, hierarchical storage manager, data indextables, processing data, and other materials associated with controllingand managing cryptocurrency mining machines 16, 18, 20 and nth. Database24 may be configured as a relational database that includes one or moretables of rows and columns that can be searched or queried according toa particular query language, such as a version of Structured QueryLanguage (SQL). Alternatively, database 24 may be configured as astructured data store that includes data records formatted according toa markup language, such as a version of extensible Markup Language(XML). In other embodiments, database 24 may be implemented using one ormore arbitrarily or minimally structured data files managed andaccessible through any suitable type of application. In one embodiment,computing devices 12, 14, and/or cryptocurrency mining machines 16, 18,20, nth directly, or indirectly communicate with database 24 via,network 22. Database 24 may also include a plurality of databases.

The system and method 10 includes one or more servers 26 for data ordata file storage, management, and sharing, performing computercomputations or processes, hosting software or firmware, maintainingdata indexes, email communications, managing, storing and sharingdigital video or audio content, managing machine learningmodels/algorithms, managing artificial intelligence (AI) processes, andaccessing, retrieving, and transmitting data and information provided bythird-party service networks. For example, in one embodiment, server 26communicates with a digital currency exchange network 28 to access,retrieve, and transmit data and information associated with miningdigital currency such as profit variables including, but not limited to,block rewards, digital coin prices, electricity price, and difficulty.It is appreciated that database 24, and server 26 may include acloud-based services system or network that is managed by a third partyentity or company. These profit variables may be accessed, retrieved,and transmitted at a predetermined interval. The system and method 10for dynamically tuning cryptocurrency mining machines 16, 18, 20, nthmay be implemented as a unified or distributed system using one or morecomputing devices 12, 14, and may be implemented as part of a singlesoftware or software/hardware system, or alternatively, may bepartitioned in any suitable fashion into a number of distinct modules,procedures or other functional portions.

Communication network 22 provides electronic communication betweencomputing device 12, 14, and cryptocurrency mining machines 16, 18, 20,nth, and/or with other electronic peripheral devices including forexample, database 24, server 26, printers, web cams, sensors, monitors,or detectors, video systems, cameras, lights, and with TOT devices. Itis understood that each computing device 12, 14, and each cryptocurrencymining machine 16, 18, 20, nth can electronically communicate with eachother over the communication network 22 as well. Communication network22 may include a wired or wireless communication network including aWLAN (wireless local area network, such as Wi-Fi (IEEE 802.11)), WPANS(wireless personal area networks, such as (IEEE 802.15), Infrared,ZigBee), WMAN (wireless metropolitan area network, such as WiMAX (IEEE802.16)), WWAN (wireless wide area networks, internet), and GAN (globalarea network), a telephone network, (e.g., analog, digital, wired,wireless, PSTN, ISDN, or XDSL, a mobile wireless communication system,such as 3G, 4G, 5G, an internet-protocol based communication system, orother radio network (RF), cable network, satellite network, opticalnetwork, the internet, via Ethernet, or intranet system, LAN (Local AreaNetwork), PAN (Personal Area Network), MAN (Metropolitan Area Network),and WAN (Wide Area Network). Communication network 22 may include avariety of communication or information exchange components orperipherals, including, but not limited to, one or more base stations,proxy servers, routers, switches, repeaters, Ethernet hubs, wired orwireless data pathways, or modems, that are configured to direct and/ordeliver data and/or information.

Turning to FIG. 2 , there is shown a schematic block diagram of acomputing device 12 used in controlling and managing one or morecryptocurrency mining machines 16, 18, 20, nth over the communicationnetwork 22 when mining digital currency. The functional description andoperation of computing device 12 is attributed to additional computingdevices. Computing device 12 includes a processor(s) 30, and memory 32for hosting miner management software provided in a computer-executablemedium, and processing the computer-executable program instructions,computer code, and computer application programs, or software tocommunicate with the dynamic tuning firmware for controlling, andmanaging each cryptocurrency mining machine 16, 18, 20, nth. Memory 32communicates with processor(s) 30 and other components via, anelectrical communication bus 34. Examples of memory 32 may includestatic random access memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/flash-type memory, machine-readable media, read onlycomponent, or any combination thereof. In one exemplary embodiment,memory 32 may include machine-executable instructions, programs, orapplications 36 embodying machine learning models/algorithms 38, andhierarchical storage manager 40 that work in unison with the dynamictuning firmware 100, shown in FIG. 4 . Each computing device 12, 14includes an I/O interface 42 electrically coupled to bus 34 foraccommodating communication with a display monitor 44, and input devices46. Examples of inputs devices 46 may include a keyboard, an electronicpen or pointer, an optical scanner, a touchpad, an electronic mouse,audio input device such as a microphone, a video capture device, or atouch screen. A network interface 48 is provided to electrically connectand communicate with communication network 22. The network interface 48may include any network interface card including for example, anEthernet network interface card, a wireless network interface card, orone or more modems. A power supply unit 50 provides power to electricalcomponents. Computing device 12 may include a storage device 52 thatincludes a hard disk drive, a magnetic disk drive, an optical discdrive, a solid-state memory device, memory dongle, magnetic storagemedia, or any combination thereof. The storage device 52 can include anexternal storage device, such as a removable disk drive, memory stick,or flash drive that is removably attachable to computing device 12 viaan electrical connector or interface. In one example, storage device 52,and memory 32 may provide volatile, and/or non-volatile storage ofmachine-readable instructions, data structure, program modulesassociated with machine learning models/algorithms 36, hierarchicalstorage manager 40, and dynamic tuning firmware 100. There is provided aperipheral interface 54 for accommodating peripheral output devicesincluding printers, speakers, or visual/audible indicating devices. Theperipheral interface 54 may include a serial or parallel portconnection, USB or HDMI connection, or other compatible electricconnections associated with peripheral devices.

Turning to FIG. 3 , there is shown a side view of a cryptocurrencymining machine 16 showing a portion of a side wall 56 removed toillustrate a schematic representation of operating components. In oneexemplary embodiment, each cryptocurrency mining machine 16, 18, 20, nthincludes a variety of electronic components that are electricallymounted on printed circuit boards physically secured within anenclosure. Functional electronic components generally include aninput/output (I/O) interface card 58 for electronically communicatingwith computing devices 12, 14, or with other cryptocurrency miningmachines 18, 20, nth via, communication network 22. There is provided amother board 60 including processor(s) and memory for hosting thedynamic tuning firmware 100, a plurality of hash boards 62, 64, a powersupply 61 for powering the electronic components, and a cooling moduleincluding a pair of fans 68, 70 to draw heat away from electroniccomponents including integrated circuit (IC) mining chips. In onealternative embodiment, the cooling module may employ a liquid, or gascooling system that employs nitrogen, water, or other cooling agent tocool mining chips. Power supply 61 includes all necessary electroniccircuitry and components including for example, step-down transformers,voltage regulators, filters, fuses, and other electronics for managinginput power, and generating and delivering a regulated, voltage supplyto electronic components. Supply voltage measuring circuitry may beprovided to measure power and/or voltage delivered from the voltagepower supply 61 during a start-up chip voltage phase when poweringintegrated circuit chips, and to alert users when voltage power supply61 is not functioning properly. The supply voltage measuring circuitrymay be selectively disabled by users.

The I/O interface 58, mother board 60, and power supply 61 can beenclosed within housing 56, or alternatively housed separately. Eachhash board 62, 64 includes a predetermined number of mining chips 66,68, 70, 72 that are particular designed for mining digital currency. Itis appreciated that both the number of hash boards 62, 64, and miningchips 66, 68, 70, 72 shown are for illustrative purposes only, and thateach cryptocurrency mining machine 16, 18, 20, nth may include anynumber of hash boards 62, 64 each having any number of mining chips.Each hash board 62, 64 includes a PIC (peripheral interface controller)denoted at 74, 76 for electrically communicating with respective motherboards 60, and mining chips 66, 68, 70, and 72 of each cryptocurrencymachine.

In the preferred embodiment, each mining chip 66, 68, 70, 72 includes anapplication-specific integrated circuit (ASIC) chip that each include aSHA-266 algorithm for mining a specific digital currency attributed tobitcoins. ASIC chips provide smaller volume, lower power consumption,and enhanced reliability. In one alternative embodiment, ASIC chips 66,68, 70, 72 may be replaced with field programmable gate array chips(FPGA), or Graphic Processing Unit chips (GPU), or any combinationthereof. Users can program FPGA chips with different algorithmsdepending on the digital currency mined.

Certain condition parameters generally impact the functionality, andoperating efficiency of cryptocurrency mining machine 16, 18, 20 andnth. For example, high operating temperatures may compromise theoperating performance of ASIC chip 66, 68, 70, 72, and/or hash boards62, 64, potentially causing damage if not managed properly. Eachcryptocurrency mining machine includes a variety of sensors, ordetectors for continuously monitoring the temperature of ASIC chip 66,68, 70, 72, the temperature of hash boards 62, 64, the rotationaldirection and/or speed of fans 68, 70, the internal temperature ofhousing 56, the environmental temperature, or humidity, in real-time. Inone embodiment, board temperature sensors 78, 80 are provided on eachrespective hash board 62, 64, and an inner housing temperature sensor 82is also provided within the inner cavity of each housing 56 to measurethe internal temperature of cryptocurrency mining machines. Anenvironment temperature sensor 84 may be affixed to the external surfaceof each cryptocurrency mining machine 16, 18, 20 nth for measuringenvironmental temperature in which the mining machines operate. On-chiptemperature sensors 86, 88, 90, 92 are also provided to measure thetemperature of each ASIC chip at start-up, and during operation. Eachon-chip temperature sensor 86, 88, 90, 92 may include miniaturethermocouples, resistance temperature detectors, thermistors, or othersemi-conductor based integrated circuits. It is appreciated that anynumber of temperature sensors or detectors can be implemented to measurevarious temperatures, or other characteristics such as humidityassociated with cryptocurrency mining machine 16, 18, 20 and nth. Forexample, fan sensors 94, 96 are provided to measure, or detect therotational speed of the fans. Such fan sensors 94, 96 may includeencoders, motion detectors, or voltage/current circuitry. Sensors 94, 96electrically communicate with a fan control module 67 that is incommunication with mother board 60. The fan control module 67 includespulse width modification measuring and detecting circuity. The fancontrol module 67 monitors, measures, and dynamically controls the speedof fans 68, 70 to manage the temperature of ASIC chip 66, 68, 70, 72,and/or hash board 62, 64 to prevent damage, and overheating. Sensors 78,80, 82, 84, 86, 88, 90, 92, 94, 96 are all in electrical communicationwith mother board 60 as well for managing control of fans 68, and 70via, fan control module 67.

Turning now to FIG. 4 , there is provided a block diagram of a dynamictuning firmware 100 including software, instructions, computer code,application(s) and/or program(s) for effectively and efficientlycontrolling and managing the operation of ASIC chips 86, 88, 90, 92, andvarious functionalities of each cryptocurrency mining machine 16, 18,20, nth when mining digital currency. The dynamic tuning firmware 100includes a tuning preset configuration block 102 in functionalcommunication with a tuning process block denoted at 114. The tuningpreset configuration block 102 provides users with selectable interfaceconfigurations to preconfigure various operational parameters includingchip profile configurations, user-defined profile configurations, targetchip temperatures, and profit analysis and variables, and forselectively disabling, or enabling certain modes of operation affiliatedwith each cryptocurrency mining machine. The tuning preset configurationprovides a miner profile configuration 104 for generating a minerprofile configuration screen to preset or preconfigure chip profileconfigurations, and target chip temperatures; a configuration multiplier106 for generating a configuration multiplier screen to preset orpreconfigure chip profile configurations and target chip temperaturesfor a plurality of cryptocurrency mining machines simultaneously; atuner configuration 108 for generating a tuner configuration screen tomanually preset or preconfigure user-defined chip profileconfigurations; a profitability configuration 110 for generating aprofit configuration screen to preset profit variables used indetermining mining profit, and other configuration 112 associated withgenerating other interactive screens for managing other functionalitiesof the dynamic tuning firmware 100. The tuning process block 114includes various processes and functionalities associated withcontrolling and managing cryptocurrency mining machines 16, 18, 20, nth,and more particularly, controlling and managing the operation of ASICchips 86, 88, 90, 92, and other electronic components. In onenon-limiting embodiment, the tuning process block 114 includes a presetchip profile configuration 116 providing a plurality of preset chipprofile configurations that are predefined and stored for use. Eachpreset, chip profile configuration includes, inter alia, a voltageprofile range including a maximum and minimum chip voltage, and afrequency profile range including a maximum and minimum chip frequency;a chip and/or hash board temperature control/management 118 thatelectrically communicates with mother board 60 via, PIC 74, 75 tocontrol fans 68, 70 for managing the operating temperature of ASICchips, and/or hash boards; auto-tuning control 120 for dynamicallyadjusting voltages, and frequencies associated with the voltage andfrequency profile range, respectively, to determine tuned parametersincluding a target chip voltage, and target chip frequency to provideefficient power usage, and optimal hash rate when applied to ASIC chips;a metric data collection and analysis block 121 for collecting,monitoring, processing, managing, analyzing, and storing variousperformance metric data or information used during auto-tuning; a profitanalysis process 124 including profit variable management (in anexemplary embodiment, profit variable management may includeestablishing communication with an external device such as server 26and/or digital currency exchange network 28 via an external network suchas network 22, and then retrieving at least one profit variable from theexternal device) associated with mining digital currency, and a profitalgorithm that employs the profit variables (in an exemplary embodiment,this may include calculating an estimated profitability of a mining chipbased on one or more profit variables) to apply tuned parameters thatprovide the highest profit to ASIC chips (in an exemplary embodiment,this may include adjusting a chip voltage supplied to a mining chipand/or adjusting a chip frequency of a mining chip in order to maximizethe estimated profitability); an auto-switch control 126 that operatesto selectively switch between chip profile configurations based onvarious condition parameters to find tuned parameters; a presetuser-defined profile configuration 128 for users to manually presetuser-defined chip profile configurations; a machine learningmodels/algorithms 130 for applying machine learning models/algorithmsduring the auto-tuning process to learn a plurality of tuned parametersassociated with chip profile configurations; a hierarchical storagemanager 132 optionally employed to store and classify learned tunedparameters according to certain rules or policies; a chip settingprocess 134; and a start-up phase and core initialization process 136for powering on the ASIC chips and other electronic components, andinitializing the chip cores to begin auto-tuning. It is appreciated thatother dynamic tuning processes associated with controlling and managingcryptocurrency mining machines 16, 18, 20, nth may be provided in tuningprocess block 114. For example, additional tuning processes may includeconfiguration and functionality of peripheral interface controllers(PIC), updating features associated with firmware, machine learning, orminer management software.

In a preferred embodiment, the dynamic tuning firmware 100 is stored ina machine-readable executable medium, or a non-transitorymachine-readable executable medium hosted in memory of mother board 60provided on each cryptocurrency mining machine 16, 18, 20, and nth, asillustrated in FIG. 3 . Alternatively, the dynamic tuning firmware 100may be hosted on computing devices 12, 14, or on a cloud-based systemwhere cryptocurrency mining machines can access, and retrieve variousfunctionalities or processes associated with the dynamic tuning firmwareprograms, computer code, and instructions, via, communication network22. The dynamic tuning firmware 100 provides the necessary programuser-interfaces (e.g. interface screens) needed for users tocommunicate, interact, manage, control, and exchange information withcryptocurrency mining machine 16, 18, 20 and nth. In one non-limitingembodiment, the user-interface may include a graphical user interface, asoftware interface, a hardware interface, or any combination thereof forenabling users to view, edit, change, add, create, manipulate, input,save or store, print, command, submit, transfer, manage, navigate, andimport/export, any and all data, information, bits, values, elements,figures, symbols, characters, terms, numbers, or graphs, associated withcryptocurrency mining machines 16, 18, 20, nth. It is appreciated thatinterface screen generated by the tuning preset configurations 104, 106,108, 110, 112 may include any number of screens each including anynumber of icons, banners, drop down menus, entry boxes, designatedinputs, check boxes, tabs, inserts, pop-ups, query boxes, rows orcolumns of data or information, libraries, spreadsheets, expandablewindows, scrolls, tables, menus, and other designed formats orconfigurations to permit users to view, modify, enter and remove data,information, values, digits, bars, charts, colors, or percentage values.

Users access and communicate with each cryptocurrency mining machine 16,18, 20, nth via, computing device 12, 14 over the communication network22. Users can access each designated cryptocurrency mining machinedirectly without authentication, or alternatively, through userauthentication protocols. To gain direct access to each cryptocurrencymining machine 16, 18, 20, nth, users enter an IP address, associatedwith each designated cryptocurrency mining machine, in an address bar ofa control program (e.g. browser) provided on each computing device 12,14. Upon entering the IP address, users are presented with an interfacehome screen of the dynamic tuning firmware 100. In an alternativeembodiment, users enter a uniform resource locator (URL) in the addressbar of the control program to gain access to a log-in page that requiresuser-authentication. The log-in page functionally supportsauthentication/access protocols including a single or multi-tieredauthentication process protocol. In general, a user may performauthentication based on various factors including for example, username,password, passphrase, PIN, secret question, secret answer, or possessionof a machine readable secret data such as encryption key, or via,biometric attributes such as fingerprint, palm, voice characteristics,or iris pattern. In one example, users enter a user name, and passwordto satisfy the authentication protocol to gain access to tuning presetconfiguration screens provided in the tuning preset configuration block102, of FIG. 4 . In one embodiment, the authentication protocol maygenerate a security code that is submitted to a user's smartphone, via,a SMS text, to confirm the identity of the user.

Turning now to FIG. 5 , there is shown a mining profile configurationscreen provided via, the miner profile configuration 104 of the dynamictuning firmware. The mining profile configuration screen provides adashboard 200 of function tabs that correspond to a plurality ofsub-function tabs each associated with a user interface screen. In oneexample, function tabs include a system tab 201, a miner configurationtab 204, a miner status tab 206, and a configuration multiplier tab 208.Upon selecting the miner configuration 204 function tab from dashboard200, there is presented a plurality of sub-function tabs including, inone non-limiting example, general settings, mining profiles 210, chain(hash board) frequency settings, ASIC chip frequency settings, auto-tune212, and hotel/fee. In selecting sub-function tab 210, the dynamictuning firmware initiates instructions or computer code to generate amining profile configuration screen 202 used to preset or preconfigurechip profile configurations, target chip temperature values andsettings, and enable various modes of operation including an auto-switchmode 232, a soft-start mode 237, and a chip warmup mode 228.

Instructions, and computer code of the dynamic tuning firmware 100initiates an auto-tuning process 120 to process selectable chip profileconfigurations provided at 116, or user-defined profile configurationsprovided at 128 of FIG. 4 , to dynamically adjust voltages andfrequencies provided in the voltage and frequency profile range,respectively, associated with chip profile configurations to find atuned parameters that when applied to the ASIC chips 66, 68, 70, 72provide low power consumption, and high hashrate values to effectivelyand efficiently operate cryptocurrency mining machines 16, 18, 20, nthwhen mining digital currency. Each chip profile configuration is presetor predetermined in advance, and stored. A plurality of chip profileconfigurations are provided via, drop down menu 230, as shown in FIGS. 5and 6 . The plurality of chip profile configurations are initiallydetermined during a chip profile set-up phase based on chipmanufacturing specifications, and other factors where chip voltages, andchip frequencies are applied to ASIC chips 66, 68, 70, 72 ofcryptocurrency mining machines, over an x number of times, to findvoltage profile ranges that provide efficient power usage values, andfrequency profile ranges that provide optimal hashrate values whenapplied to ASIC chips. Each voltage profile range includes a maximumchip voltage, and a minimum chip voltage, a frequency profile range thatincludes a maximum chip frequency, and a minimum chip frequency, avoltage increment or decrement value, and a tuning cycle. During anauto-tuning process, the voltage and frequency values provided in eachvoltage and frequency profile range associated with each selected chipprofile configuration, are dynamically adjusted until a target chipvoltage, and target chip frequency that provides the lowest powerconsumption at the highest chip hashrate is found. Auto-tuning adjuststhe chip voltage, and chip frequency, of each profile range, todetermine a target chip voltage, and target chip frequency that providesfor low power usage, and optimal hashrate to optimally manage chiptemperature and operating costs, and to garner the highest profits. Theconsumed power usage, and hashrate value associated with each targetchip voltage, and target chip frequency can be measured in real-timeusing electronic circuitry, IC's, or electronic modules. Alternatively,the consumed power usage, and hashrate values of each ASIC chip can becalculated using specific, mathematical equations or algorithms.

In one embodiment, each chip profile configuration is assigned a profileidentifier for categorizing chip profile configurations according toincreasing or decreasing voltage, and frequency values provided in eachvoltage and frequency profile range, or increasing and decreasing powerusage value in watts. For example, a profile identifier 53 may include apower usage of 2160 watts, a maximum chip voltage of 18 volts, a minimumchip voltage of 5 volts, a maximum chip frequency of 660 MHz, a minimumchip frequency of 180 MHz, a voltage increment or decrement value of 0.1volts, and a tuning cycle of 5. A profile identifier 57 may include apower usage of 2660 watts, a maximum chip voltage of 23 volts, a minimumchip voltage of 8 volts, a maximum chip frequency of 700 MHz, a minimumchip frequency of 300 MHz, a voltage increment or decrement value of 0.2volts, and a tuning cycle of 7. As illustrated in FIG. 5 , a resetprofile operator 235 allows users to clear previously stored chipprofile configurations from memory. In one embodiment, enabling thereset profile operator 235 may provide a reset profile screen (notshown) for users to preset dates, and/or times at which all, or aselected number of, chip profile configurations are cleared from memory.

In embodiment, each chip or user-defined profile configuration mayinclude solely a wattage value that is selectively retrieved duringauto-tuning to determine target chip voltages, and target chipfrequencies that provide for the lowest power consumption at the highesthashrate for mining digital currency. For example, each wattage valuemay include a predetermined voltage and frequency profile range employedduring auto-tuning to determine tuned parameters. Each chip profileconfiguration including a wattage value may be assigned a profileidentifier such as a numeric number, or alphabet letter, forcategorizing wattage values according to increasing or decreasingwattage values. For instance, a wattage value of 1500 watts may beassigned a profile identifier as a number of 50, where as a wattagevalue of 2000 watts is assigned a higher profile identifier as number52. Thus, drop down menu 230 may include solely wattage values, numericnumbers associated with wattage values, or both. In another embodiment,each profile configuration may include terahash values associated withpredetermined voltage and frequency profile ranges employed duringauto-tuning to determine tuned parameters.

Dynamically adjusting chip voltages, and chip frequencies provided inthe voltage and frequency profile ranges to optimize the performance ofASIC chips 66, 68, 70, 72 is based on various condition parametersincluding temperature. Managing chip temperature is important to preventdamage, and instability of ASIC chips while maintaining the efficientoperation of cryptocurrency mining machine 16, 18, 20, and nth. Managingthe temperature of the ASIC chips is a function of the chip and/or hashboard temperature control and management functionality 118 of the tuningprocess 114 as provided by the dynamic tuning firmware 100 in FIG. 4 .As provided in the mining profile configuration screen 202, shown inFIG. 5 , chip temperature ranges are preset by users in advance. Uponpresetting chip temperature ranges, a target chip temperature value(given in a range of 0 to 75 C in one example) is entered in designatedbox 214, a maximum (downscale) chip temperature value is provided indesignated box 216, and a minimum (upscale) chip temperature value isentered in box 218. In one example, there is provided a target chiptemperature of 70 degrees for identifying an ideal operating temperatureof the ASIC chips, a maximum chip temperature of 85 degrees, and aminimum chip temperature of 60 degrees at which the ASIC chips are tooperate. To effectuate such settings, there is provided on-chiptemperature sensors 86, 88, 90, 92 to continuously measure the operatingtemperature of each ASIC chip 66, 68, 70, 72, and generate electricalsignals corresponding to the measured chip temperatures. The generatedelectrical signals are transmitted to mother board 60 for analyzing andprocessing via, PIC 74, 76. Mother board 60 electrically communicateswith fan control module 67 to control the operating speed of fans 68, 70of each cryptocurrency mining machine 16, 18, 20, nth, to cool ASICchips 66, 68, 70, and 72. Fans 68, 70 are controlled to forciblycirculate the air, and draw heat away from the ASIC chips to regulateand manage chip temperature. If the measured chip temperature of one ormore ASIC chips exceeds a maximum chip temperature preset at 216, themeasured data is processed by the mother board 60, and the mother board60 subsequently delivers a control signal to fan control module 67 toincrease the pulse width modification (PWM) for increasing therotational speed of fans 68, 70 forcibly drawing heat away from the ASICchips 66, 68, 70 and 72. However, if the temperature of one or more ASICchips falls below a minimum (upscale) chip temperature provided in 218,the mother board 60 delivers a control signal to fan control module 67to decrease PWM of fans 68, 70 which decreases the rotational speed offans 68, 70 to save power as a result of the ASIC chips producing lessheat. Rotational speed sensors 94, 96 are provided to measure therotational speed, and/or direction of fans 68, 70. Fan control 67includes a PWM detecting and measuring circuit or module to detect,measure and control the pulse width modification signal delivered tofans 68 and 70.

In certain environment, colder temperatures may affect the performanceof ASIC chips 66, 68, 70, 72, and compromise the operation ofcryptocurrency mining machine 16, 18, 20, and nth. For example,cryptocurrency mining machines 16, 18, 20, nth may operate in colderenvironments where the temperature of ASIC chips fall below normaltemperature ranges. To overcome colder temperatures, each cryptocurrencymining machine 16, 18, 20, nth is configured to initiate a chip warm-upcycle for warming the ASIC chips 66, 68, 70, and 72 to acceptabletemperature levels before operating to full capacity. When enabling thechip warm-up cycle via, 228 in FIG. 5 , a predetermined chip voltagesupplied by the power supply 61, is delivered to the ASIC chips. In oneexemplary embodiment, the chip warm-up signal may include a dc voltageincluding a constant predetermined voltage, a voltage based on apredetermined duty cycle, or a voltage that gradually increases in valueuntil reaching a maximum value. The chip warm-up signal may range from0.1 volts to 5 volts dc. A graphical visual or audible indicator may beimplemented via, the dynamic tuning firmware and/or via hardware, or ahardware visual or audible indicator such as light emitting diodes, orbuzzers, may be implemented, to alert users when the proper warm-uptemperature of each ASIC chip 66, 68, 70, 72 has been reached. Theon-chip temperature sensors 86, 88, 90, 92 inform the mother board 60when the appropriate chip temperature has been reached, via PIC 74, and76. It is appreciated that the chip warm-up cycle can be disabled byusers when cryptocurrency mining machines are operating in warmerclimates. In one embodiment, the chip warm-up cycle may be accomplishedby using one or more miniature, electric heaters disposed on, oradjacent to, each ASIC chip, or located within the internal cavityhousing 56 of each cryptocurrency mining machine. The electrical heaterscan be controlled by PIC 74, 76 via, mother board 60 of eachcryptocurrency mining machine.

With continued reference to the mining profile configuration screenshown in FIG. 5 , there is provided additional settings for dynamicallytuning each cryptocurrency mining machine that include: downscale ifauto-tune fails function 220 where if the cryptocurrency mining machine16, 18, 20, nth fails to auto-tune correctly based on a predefinedtuning cycle, the auto-switch feature of each cryptocurrency miningmachine will selectively switch to another chip profile configuration inan effort to find tuned chip parameters that provide for low powerconsumption, at a high hashrate; a downscale if chip temperature and/orhash board temperature is higher function 216 where if the chiptemperature and/or hash board temperature is higher than the maximumchip temperature value chip, the auto-switch feature of eachcryptocurrency mining machine 16, 18, 20, nth selectively switchesbetween chip profile configurations to find tuned parameters (the targetchip voltage, and target chip frequency) needed to manage temperaturescloser to the preconfigured target chip temperature value provided: adownscale profile if PWM (pulse width modification) is higher function222, where if automatic fan control is activated to regulate chiptemperature of ASIC chip 66, 68, 70, 72, and the speed of one or bothfans 68, 70 is operating over an x %, for example over 90%, thecryptocurrency mining machine 16, 18, 20, nth the auto-switch featureallows each cryptocurrency mining machine to selectively switch betweenchip profile configurations to tuned parameters that provide lower powerconsumption at the highest hashrate when applied to the ASIC chips tomanage chip temperature, and correspondingly increase the PWM toincrease fan speed; an upscale profile if chip temperature and/or hashboard temperature lower function 218, where if the chip temperatureand/or hash board temperature is lower than a minimum chip and/or hashboard temperature, the cryptocurrency mining machine 16, 18, 20, nthselectively or automatically switches between chip profileconfigurations to find tuned parameters that provide a higher hashrateto mine more aggressively under cooler temperatures, while managingpower consumption; an upscale profile if PWM is lower function 224,where if automatic fan control is activated to regulate temperature ofASIC chip 66, 68, 70, 72, and the speed of one or both fans 68, 70 islower than x %, for example lower than 18%, the cryptocurrency miningmachine 16, 18, 20, nth will automatically switch between chip profileconfigurations to find tuned parameters that provide a higher powerusage and an increased hashrate, where fan control module 67 maygradually increase the PWM to gradually increase the speed of fans 68,70 as the increase in power usage and hashrate begins to generate anincrease in chip temperature; and a maximum upscale profile function 226which represents a maximum chip profile configuration, or maximum chipvoltage, and/or chip frequency that the cryptocurrency mining machine16, 18, 20, nth will operate at. So if cryptocurrency mining machine 16,18, 20, nth are operating in a cold environment, the maximum upscaleprofile places a limit on the amount of power consumed by ASIC chips.This feature is useful in situations where there may not be enough poweravailable by the power supply thus preventing the cryptocurrency miningmachine 16, 18, 20, nth from demanding too much power and possiblypreventing tripping of power circuit breakers when operating in coolertemperatures.

Instructions and/or computer code of the dynamic tuning firmware 100initiates operation of the auto-switch process 126 for auto-tuning thecryptocurrency mining machines. When auto-switch is enabled, auto-tuningselectively switches between chip profile configurations provided indrop down menu 230 to dynamically adjust the chip voltage and chipfrequency associated with each voltage and frequency profile range todetermine the optimal target chip voltage and target chip frequency needto effectively manage power usage, and optimal performance of ASICchips. To save time, and effort, auto-switch is configured for multiplecryptocurrency simultaneously, eliminating the need to preconfigure eachmachine separately. As shown in FIG. 7 , through dashboard 200, usersselect a configuration multiplier function tab 208, and a sub-functionconfiguration tab 302 to access a configuration multiplier screengenerally denoted at 300. A drop down menu 304 similar to drop down menu230, provides a plurality of chip profile configurations for initiallyselecting a chip profile configuration. After selecting a chip profileconfiguration from the drop down menu 304, users enable the auto-switchmode 306. The configuration multiplier screen 300 also providesadditional settings for managing and controlling multiple cryptocurrencymining machines 16, 18, 20, nth similar to those in FIG. 5 . Suchadditional settings include: downscale if auto-switch fails function308, downscale if chip temperature and/or hash board temperature ishigher function 310, downscale profile if PWM (pulse width modification)is higher function 312, upscale profile if chip temperature and/or hashboard temperature lower function 314, upscale profile if PWM is lowerfunction 316, and a maximum upscale profile function 318 represents themaximum chip profile configuration.

Referring to FIG. 8 , users may enter user-defined profileconfigurations rather than employ preset chip profile configurations asprovided in the drop down menu 230 in FIG. 5 . The user-defined profileconfiguration functionality 128 of the dynamic tuning firmware 100generates an interface tuner configuration screen 400 when initiated byusers. To initiate access to the tuner configuration screen 400, usersinitiate the function tab denoted miner configuration 204 from dashboard200, and initiate the associated sub-function auto-tune tab 412instructing the dynamic tuning firmware to provide the tunerconfiguration screen 400. It is noted that when selecting the userprofile operative from drop down menu 230, the auto-switch profile 232is automatically disabled since the user-defined chip profileconfiguration includes a single configuration arrangement andauto-switching between different profile configurations is notconfigured. Tuner configuration screen 400 provides an auto-tune modeoperator 402 to initiate auto-tuning of the preconfigured user-definedprofile configuration for auto-tuning one or more designatedcryptocurrency mining machines. A user-defined profile configuration isconfigured in each designated entry including a frequency range profile404 having a maximum and minimum chip frequency, a voltage range profile406 having a maximum and minimum chip voltage, a voltage increment ordecrement value 408, and a tuning cycle 410. It is appreciated that thevalues provided in the user-defined profile configuration may includethe same or different values provided in the chip profileconfigurations. Tuner configuration screen 400 may provide one or moredrop down menus that include a plurality of predefined user-definedvoltage and frequency profile ranges. As shown in FIG. 8 , tunerconfiguration screen 400 includes an operative entitled, ignore minimumvoltage limit 412 which instructs auto-tuning to ignore the minimumvoltage provided in the voltage profile range and to determine a lowertarget chip voltage in the event chip instability has not yet beendetermined during the core initialization process.

After configuring each designated cryptocurrency mining machine 16, 18,20, nth by either enabling auto-switch and selecting a chip profileconfiguration, or by selecting user profile for disabling auto-switchmode and manually entering a user-defined profile configuration, eachcryptocurrency mining machine 16, 18, 20, nth is then subsequentlypowered-on during a start-up phase which is supported by the start-upphase/core initialization process at 136 of the dynamic tuning firmware,as shown in FIG. 4 . During the start-up phase, the mother board 60, ofeach cryptocurrency mining machine 16, 18, 20, nth, initiates an initialstatus check to determine the operational status of the cooling module,i.e. fans 68, 70, the temperature of ASIC chips, and/or hash boards, andthe operational status of some or all of sensors 78, 80, 82, 84, 86, 88,90, 92, 94 and 96. In one embodiment, the status check process checksthe operational status of fans 68, 70 if enabled, by either detectingand/or measuring the electrical pulse width modification (PWM) signalsdelivered to the fans 68, 70, via, fan control 67, or by monitoring therotational speed of the fans 68, 70, via sensors 94, 96. It isappreciated that during the start-up phase, each cryptocurrency miningmachine 16, 18, 20, nth may begin a soft-start process where therotational speed of the fan 68, 70 is increased gradually over apredetermined time period until reaching a maximum speed. The soft-startprocess is enabled by users at 237 in FIG. 5 . If during the statuscheck, fans 68, 70 are found inoperative, the start-up phase isterminated, and users are alerted via, a hardware and/or softwareindicator. It is appreciated that the start-up phase may initiate astatus check for other types of cooling modules or systems as wellincluding for example, checking the level and/or flow of a coolant, orgas such as nitrogen gas, checking on the operation of a refrigerantsystem, or the operation of electric cooling devices such as Peltiercooling devices. The status check phase also checks or measures thetemperature of ASIC chip 66, 68, 70, 72 and/or hash board 62, 64 via,and the operational status of on-chip temperature sensors 86, 88, 90,92, and/or PCB temperature sensors 78, and 80. If for example, the chiptemperature of one or more ASIC chips, and/or the temperature of eitheror both hash boards, falls below, or above a predetermined temperaturerange, or if a chip temperature is deemed to be too low as a result ofoperating in a cold environment, cryptocurrency mining machine 16, 18,20, nth begins a temperature cool-down process to lower the temperature,or a chip warm-up cycle to warm-up the ASIC chips until reaching apredefined target or threshold chip temperature. In one embodiment,there may be provided a sensor operator for users to enable or disablethe status check of some or all components including the temperaturesensors. In another embodiment, the selector operator may provide a dropdown menu where users can select the components that are needed to bechecked during the status check process of the start-up phase. Once thestatus check phase is completed, a core initialization process begins.

Through instructions, and/or computer code provided by the coreinitialization process 136 of the dynamic tuning firmware 100,electrical communication is initiated between mother board 60, powersupply 61, and PIC 74, 76 to deliver a low chip frequency of approx. 5MHz, and a start-up chip voltage to all ASIC chips 66, 68, 70, 72.During core initialization, the start-up chip voltage delivered to theASIC chips is gradually increased, over a predetermined time, to preventdamage to, and instability of, ASIC chips 66, 68, 70, 72 until reachinga maximum operating chip voltage. Alternatively, the start-up chipvoltage can be rapidly increased until reaching the maximum operatingchip voltage in a shorter time period. The maximum operating chipvoltage is defined as an initial power value calculated from the numberof voltage domains. For example, each cryptocurrency mining machine 16,18, 20, nth may have 12 voltage domains where an initial voltage foreach ASIC chip is approximately 1.75 volts resulting in a maximumoperating chip voltage of 12 times 1.75 volts=21 volts. Thus, duringcore initialization, the start-up chip voltage is gradually, or rapidlyincreased to 21 volts.

Once the maximum operating chip voltage for a given chip frequency rangeis reached, the core initialization process initiates a function statussequence to determine operation, and electrical communication responseof each ASIC chip 66, 68, 70, 72 and/or hash board 62 and 64. Thefunction status sequence undergoes an evaluation process which measures,calculates, analyzes and/or monitors any of: clock speed (hashrate),maximum operating chip voltage, maximum chip frequency, and chiptemperature via, on-chip temperature sensors 86, 88, 90, 92. If duringthe function status sequence one or more ASIC chips are found tofunction poorly, the associated cryptocurrency mining machine 16, 18,20, nth re-initiates a core initiation process on the poorly functioningASIC chips in an effort to improve performance. The core initiationprocess may occur over an x number of times such as 5 times, or over anx time period such as every 2 minutes, or 15 minutes. If after apredefined number of times, or period of time, some ASIC chips 66, 68,70, 72 are still found to function poorly, the hash board 62, 64associated with the non-functional ASIC chips is disabled, and the coreinitiation process continues analyzing other ASIC chips until thefunctional status sequence of all ASIC chips and/or hash boards iscompleted. It is understood that in one embodiment, an x number, orgroup of ASIC chips 66, 68, 70, 72 may be functioning poorly before thecore initiation process is re-initiated, or terminated. For example, inone scenario, core initiation process may be terminated only if it isdetermined that a hash board 62 includes 5, 10, or 15 ASIC chips thatare functioning poorly. After verifying functionality of each ASIC chip66, 68, 70, 72, and/or hash board 62, 64, the maximum operating chipvoltage of each ASIC chip is adjusted, via a predetermined voltagevalue, to reach the maximum chip voltage provided in the voltage profilerange of the selected chip profile configuration, or user-definedprofile configuration. Subsequently, the low chip frequency issubsequently increased gradually, a predefined frequency value, to reacha maximum chip frequency provided in the frequency profile range of theselected chip profile configuration, or user-defined profileconfiguration. Alternatively, the low operating chip frequency can beincreased rapidly to reach the maximum chip frequency within a shortertime period. Once the maximum chip voltage, and maximum chip frequencyare set for each ASIC chip 66, 68, 70, 72, the core initiation processterminates, and auto-tuning each cryptocurrency mining machine begins.

Auto-tuning optimizes the performance of ASIC chips 66, 68, 70, 72 bydynamically adjusting the chip voltage, and chip frequency provided inthe voltage and frequency profile range respectively, of each chip oruser-defined profile configuration. The chip voltage, and chipfrequency, of each selected profile configuration, is dynamicallyadjusted to determine tuned parameters (a target chip voltage, andtarget chip frequency) needed to provide the lowest ASIC chip powerusage or consumption at the highest optimal hashrate for managingoperational costs, chip temperature, and garnering higher profits whenmining digital currency. Determining the most efficient target chipvoltage for a given chip frequency range is important because chipvoltage corresponds to the power consumed by ASIC chips 66, 68, 70, and72. As the power usage or consumption of ASIC chips decreases, so doesthe cost of electricity, and the heat generated by ASIC chips 66, 68, 70and 72. The maximum chip voltage determined during the core initiationprocess, and defined in the voltage profile range of each chip profileconfiguration, is dynamically increased or decreased, a preset voltagevalue, to find the target chip voltage that is needed for overcome chipinstability to effectively manage power usage of ASIC chips. In findingthe target chip voltage, the maximum chip voltage is decreased, avoltage value as predefined in each profile configuration, untilinstability of each ASIC chip 66, 68, 70, 72 is determined within agiven chip frequency profile range. In one example, the voltage valuemay include a range of 0.01 volts to 1 volts. Chip instability is foundwhen the performance of one or more ASIC chips 66, 68, 70, 72 fallsbelow a threshold hashrate, or alternatively, when communication withthe ASIC chips is lost. Users can set a threshold hashrate value aseither a percentage of an ideal hashrate, or as a fixed hashrate value.Users may select a threshold hashrate from a drop down menu (not shown),or manually enter a threshold hashrate value in a designated entry box.A threshold hashrate value may comprise, for example, 85%, 90%, or 95%of the ASIC chips ideal hashrate. If the ideal hashrate of the ASCI chipincludes 100 hashes per second, and a threshold hashrate is set at 90%,the threshold hashrate is 90 hashes per second. When the hashrate of oneor more ASIC chips 66, 68, 70, 72 falls below the threshold hashrate of90 hashes per second, the auto-tuning process stops decreasing themaximum chip voltage to provide the lowest chip voltage for a givenfrequency range. In some circumstances, the lowest chip voltage mayinclude a value that is lower than the minimum chip voltage provided inthe voltage profile range of the selected chip profile configuration. Insuch cases, the auto-tuning process can ignore the minimum voltage limitof the voltage profile by enabling the feature at 233 in FIG. 5 . Thisfeature is enabled when chip instability is found at a chip voltage thatis lower than the minimum chip voltage provided in the voltage profilerange associated with the selected chip profile configuration. Indisabling the minimum voltage limit, the lowest chip voltage may includea value that is less than the minimum chip voltage provided in eachvoltage profile range associated with each chip profile configuration.It is noted that in a shared voltage supply configuration, a decrease inmaximum chip voltage results in a decrease in maximum chip voltage forall ASIC chips 66, 68, 70, 72 at the same time, and an increase in chipvoltage results in an increase of chip voltage for all ASIC chips at thesame time. In a non-shared voltage supply system, decreasing the maximumchip voltage results in a decrease in maximum chip voltage forindividual ASIC chips 66, 68, 70, 72 separately, one at a time, wherethe maximum chip voltage is decreased, via a same or different voltagevalue for each ASIC chip, until instability of each ASIC chip isreached, and where the chip voltage is slightly increased to provide thesame or different target chip voltage for each ASIC chip to providegreater chip stability. As such, cryptocurrency mining machines 16, 18,20, nth operating in a non-shared voltage supply system may have a sameor different target chip voltages applied to ASIC chips. In adjustingchip voltage of ASIC chips, the mother board 60 of each cryptocurrencymining machine electrically communicates with the power supply 61, andPIC 74, 76 to manage and control the power supply 61 in delivering therequisite, controlled chip voltage to the ASIC chips.

Once the lowest chip voltage has been found, auto-tuning begins thefrequency tuning of the ASIC chips 66, 68, 70, and 72. The frequencytuning process finds the optimal target chip frequency that is neededfor the ASIC chips to function at an ideal hashrate attributed to eachASIC chip to garner the most profits when mining digital currency. Theideal hashrate of each ASIC chip is generally based on a number ofhashing cores. The term, “hashrate” is a measuring unit of the totalcomputational processing power used to mine and process transactions ona proof-of-work block chain (i.e. the processing power of a bitcoinnetwork). Basically how many times an ASIC chip calculates the output ofa hash function, or the speed at which a cryptocurrency mining machinesolves a difficult mathematical puzzle. The hashrate is a measure of howmany times the network attempts to complete the difficult mathematicalpuzzle every second to earn rewards in coins which can be exchanged forreal money. Each ASIC chip 66, 68, 70, 72 includes a number of hashingcores that include block chain algorithms, such as SHA-266 algorithmsfor mining bitcoins, or ethash algorithms for mining Ethereum coins.Each hashing core performs one calculation for each clock tick of anASIC chip clock speed. As such, the ideal hashrate of each ASIC chip 66,68, 70, 72 is based on the known number of hashing cores. In oneexample, a cryptocurrency mining machine 16, 18, 20, nth has 672 hashingcores on each ASIC chip 66, 68, 70, and 72. If a chip frequency includes660 MHz, then a single ASIC chip running at 660 MHz is processing336,000 calculations per second. (672 hashing cores multiplied by 660MHz). If a hash board 62 includes 48 ASIC chips for example, it wouldprovide an ideal hashrate of 16.1 TH/s (336,000 calculations per secondfor each ASIC chip multiplied by 48 ASIC chips=16,126,000 calculationsper second). A total of 3 hash boards would yield roughly a total idealhashrate of 48 TH/s. It is noted that cryptocurrency mining machine 16,18, 20, nth may have any number of hash cores for mining different typesof digital currency.

The dynamic tuning firmware 100 includes instructions and/or computercode to perform a metric data collection and analysis process 121 forcollecting performance metric data. Electrical communication betweenelectronic components is initiated to collect, monitor, process, manage,analyze, and store performance metric data or information used forauto-tuning ASIC chips 66, 68, 70 and 72. The performance metric data orinformation may include, but is not limited to, power usage, hashrate,chip temperature, chip voltage, chip frequency, profit, internaltemperature of housing 56, environmental temperature, changes in profitvariables, humidity, rotational speed of fans 68, 70, measurement dataassociated with PWM signals, and the operational status of I/O interface58, power supply 61, mother board 60, hash boards 62, 64, sensors 82,84, 86, 88, 90, 92, and communication network 22. Performance metricdata can be measured in real-time using electronic measuring circuity ordevices, or alternatively calculated in accordance with mathematicalequations or algorithms. Metric analysis may be initiated every xseconds, minutes, or hours, like every 30 seconds, 2 minutes, or 1 hour,or at a certain time of day like at 3 p.m. every day, or when a changein value associated with a condition parameter is detected, such as achange in chip temperature, or profit. Performance metric data orinformation is stored in metric data management files, indexes, in oneor more look-up tables, or via, other data management configurationsthat are stored on each associated cryptocurrency mining machine, ondatabase 24, on server 26, on computing devices 12, 14, and/or on anexternal memory device, or any combination thereof. During auto-tuning,performance metric data including for example, hash rate/calculations asecond, is collected to determine chip hashrate, chip temperature, andpower usage. Auto-tuning dynamically adjusts the chip frequency of eachASIC chip, as provided in the frequency profile range associated witheach profile configuration, until a target chip frequency is found toprovide the hashrate that is closest to the ideal hashrate, or at orabove a hashrate threshold, of each ASIC chip. It is appreciated thatoptionally, once the target chip frequency is determined, the lowestchip voltage determined during core initialization is subsequentlyincreased slightly to provide greater chip stability. During auto-tuningchip frequency, the mother board 60, of each cryptocurrency miningmachine, communicates with PIC 74, 76 of each hash board 62, 64 tomanage and control frequency regulator/generator circuity provided ineach cryptocurrency machine to manage controlled generation and deliverof chip frequency.

In some circumstances, auto-tuning may have difficulty determining thetarget chip voltage, and target chip frequency needed to effectivelytune each ASIC chip for some reason or another. For example, every ASICchip is unique in terms of quality and manufacturing because the qualityof silicon materials used to fabricate the chips is not 100% uniform. Asa result, some ASIC chips 66, 68, 70, 72 may have certain manufacturingdefects, retain chip instability, or continuously perform poorly whenmining digital currency. In such circumstances, it may be advantageousto limit the amount of times that auto-tuning attempts to correct theperformance of poorly performing ASIC chips, and concentrate on managingthe ASIC chips that are functioning properly. As such, each chip profileconfiguration, and user-defined profile configuration, includes, interalia, a tuning cycle that represents an x number of times auto-tuning isapplied to ASIC chip 66, 68, 70, 72 in an effort to find an efficienttarget chip voltage, and optimal target chip frequency. As provided in agiven example at 410 in FIG. 8 , a representative example of a tuningcycle of 15 signifies that auto-tuning will be performed on poorlyperforming ASIC chips a total number of 15 times in an effort to findtarget chip voltages, and target chip frequencies that are needed forimproving the power usage and hashrate of the poorly performing ASICchips 66, 68, 70, and 72. When all ASIC chips are found to performwithin an acceptable power usage and threshold hashrate levels, or whenthe preset auto-tuning tuning cycle has been reached, auto-tuningterminates the process of auto-tuning, until reinitiated as a result ofchanges in performance metric data and/or condition parameters includingchip temperature, or profit.

Once auto-tuning determines the tuned parameters (i.e. the target chipvoltage, and target chip frequency) needed for ASIC chips to operate atthe lowest power to overcome instability, and at the most efficienthashrate close to the ideal hashrate, each cryptocurrency mining machine16, 18, 20, nth is rebooted to apply the tuned parameters to the ASICchips 66, 68, 70, 72 via, electrical communication of the mother board60, power supply 61, and PIC 74 and 76. If after rebooting, the appliedtarget chip voltage, and target chip frequency does not improve theperformance of ASIC chip 66, 68, 70, 72, auto-switch selectivelyswitches to another chip profile configuration and adjusts the chipvoltage, and chip frequency provided in the voltage and frequencyprofile range of the selected chip profile configuration, to determinenew tuned parameters that improve the performance of poorly operatingASIC chip 66, 68, 70, and 72. Each cryptocurrency mining machine 16, 18,20, nth is subsequently rebooted again to apply the newly determinedtuned parameters in an effort to improve the performance of the poorlyoperating ASIC chips 66, 68, 70 and 72. When the auto-switch mode isenabled, auto-switch selectively switches between chip profileconfigurations where the auto-tuning process dynamically adjusts thechip voltages and chip frequencies provided in voltage and frequencyprofile ranges associated with the selected chip profile configuration,to determine the requisite target chip voltage, and target chipfrequency needed to effectively manage chip temperature, and/or garnerhigher profits. A restart mode is provided to automatically reboot thecryptocurrency mining machines 16, 18, 20, nth, and reinitializeauto-tuning when ASIC chips perform poorly such as, when the temperatureof the ASIC chips, or hash boards 62, 64 falls outside acceptabletemperature ranges, or when hashrate values fall below a predefinedpercentage of the ideal threshold hashrate. In one example, users canpreconfigure a time that the mining machines will be rebooted. Asprovided at 239, in FIG. 5 , the preconfigured cryptocurrency miningmachines will be rebooted every 6 hours. In one embodiment, as shown at240 in FIG. 5 , users can also limit the number of times thecryptocurrency mining machines are rebooted by forcing thecryptocurrency mining machines into a sleep mode. Once the tunedparameters are determined to effectively manage power usage, chiptemperature, and/or the highest profits, the tuned parameters arelearned via, machine learning module/models/algorithm module, and storedin a tuned chip parameter data index, file, or table stored on eachcryptocurrency mining machine 16, 18, 20, nth, computing device 14, 16,and/or database 24. When auto-tuning is subsequently re-initiated basedon changes in condition parameters such as temperature or profit, eachof the tuned chip parameters are retrieved, and dynamically adjusted todetermine new target chip voltages, and new target chip frequencies.Dynamically adjusting previously tuned chip parameters not only savestime, effort, and energy, but increases time spent mining digitalcurrency thus increasing profit.

Machine learning module/models/algorithms may be implemented toautomatically configure chip profile configurations including voltageand frequency profile ranges, apply chip profile configurations, adjustchip voltages and chip frequencies of selected chip profileconfigurations, configure chip profile configurations based onpreviously tuned chip parameters, categorize previously or newly tunedchip parameters, and/or determine target chip voltages, and target chipfrequencies that provide efficient power usage and optimal hashratevalues, respectively, for effectively managing the performance of ASICchip 66, 68. 70, and 72 based on temperature or profits. The machinelearning module/models/algorithms 38, 130 may be provided on eachcomputing device 12, 14, or on each cryptocurrency mining machine 16,18, 20, nth via, dynamic tuning firmware 138, For example, eachcryptocurrency mining machine can send a request to computing devices12, 14 to receive update information, training date, machine learningmodels, or other machine learning data or information, via communicationnetwork 22. Update information may include updated versions of machinelearning models/algorithms, new machine learning algorithms, updatedweighted values, training data, operational parameters, and/or structureof the machine learning models. The machine learningmodule/models/algorithms may include unsupervised machine learning, orsupervised machine learning such as regression analysis to predictoutput values or classifications from input values. Machine learning mayemploy identifying weighing values, model settings, learning algorithms,and/or training data to generate outputs bases on inputs. Training datamay be established by testing, accessor error, re-adjusting underlyingparameters, and include, but not limited to, voltage and frequencyprofile ranges, tuning cycles, increment and decrement voltage orfrequency values, chip profile configurations, power usage orconsumption, hashrate values, profit variables, temperatures ortemperature ranges including chip temperatures, hash board temperatures,environment temperature, power usage algorithms, profit algorithms,performance metric information or data, historical use data, measured orcalculated tuned parameters, manufacturing specifications of ASIC chips,historical target chip voltages, and frequencies, chip instabilityvalues, supply voltage values, data tables, data indexes, profits, orother input data. Training data can be used for training any number ofmachine learning models. Various machine learning techniques includingdifferent algorithms, or training methods can be used to build anynumber of machine learning models that work in unison with the dynamictuning firmware to control and manage the operation and performance ofcryptocurrency mining machines 16, 18, 20, nth, via, ASIC chips 66, 68,70 and 72.

Optimizing performance of ASIC chips 66, 68, 70, 72, to promoteefficient operation of cryptocurrency mining machines 16, 18, 20, nth,is based on various condition parameters including temperature, and moreparticularly, temperature of ASIC chips, and/or hash boards. Temperaturesignificantly impacts the performance of ASIC chips and/or hash boards,and if not properly managed, can cause damage, compromise the operationof cryptocurrency mining machines, and diminish mining profits. Managingchip and hash board temperature is governed by the temperature controlmanagement process 118 of the dynamic tuning firmware 100. Temperaturecontrol management process 118 includes instructions, and/or computercode that when initiated, control and manage electric communicationbetween the mother board 60, temperature sensors 78, 80, 82, 84, on-chiptemperature sensors 86, 88, 90, 92, and fan control module 67, tocontinuously monitor the temperature of ASIC chips 66, 68, 70, 72,and/or hash boards 62, 64 while mining digital currency. Power consumedby ASIC chips generally correlates to chip temperature, and as the powerusage increases, so does the temperature of ASCI chips. Thus, one methodof controlling chip temperature is to manage the power consumed by theASIC chips, and/or hash board boards. As illustrated in FIG. 5 , thedynamic tuning firmware 100 allows users to preset, target chiptemperature ranges in advance. Guidance to proper operating temperatureranges may be provided in manufacturing product specifications whenpresetting temperature ranges. Safe operating temperature ranges forASIC chips and/or PCB boards are provided in advance by users via, atarget chip temperature 214, a maximum chip temperature 216, and aminimum chip temperature 218. In one given example, there is provided atarget chip temperature of 70 degrees, a maximum chip temperature of 85degrees, and a minimum chip temperature of 60 degrees. It is appreciatedthat the mining profile configuration screen 202 may provide designatedentry boxes for entering hash board temperature ranges as well to managethe temperature of hash boards 62, 64 within prescribed temperatureranges. When measuring temperature of ASIC chip 66, 68, 70, 72, and/orhash boards 62, 64, the on-chip temperature sensors 86, 88, 90, 92, andhash board temperature sensors 78, 80, respectively, generate andtransmit electrical signals associated with measured temperatures to themother board 60 via, PIC 74, 76 provided on each PCB hash board 62, 64.Each mother board 60 of each mining machine processes the receivedelectrical signals and communicates with the fan control module 67, andsensors 94, 96 to control fans 68, 70 to cool ASIC chips 66, 68, 70, 72,hash boards 62, 64, or other electronic components provided in eachcryptocurrency mining machine 16, 18, 20, and nth.

Additional temperature sensors may be employed to measure othercondition parameters of interest. For example, a temperature sensor 82may be employed to measure the internal temperature of eachcryptocurrency mining machine 16, 18, 20, nth, and temperature sensor 84may be used to measure the environmental temperature in whichcryptocurrency mining machines operate. Temperature sensors 82, 84 areelectrically coupled to mother board 60 which communicates with fancontrol module 67 to manage the operation of fans 68, 70 based ontemperatures associated with the internal region of housing 56, or ofthe environment in which the cryptocurrency mining machines operate.

Temperature of the ASIC chip and/or PCB hash boards is effectivelymanaged by a variety of cooling methods preconfigured by users. Onemethod includes a fan only cooling method where the fans 68, 70 areoperated at a preset speed to cool the ASIC chip 66, 68, 70, 72, and/orhash boards 62, 64 regardless of the temperature of ASIC chips, and/orhash boards. As illustrated at 229 in FIG. 5 , users can manually preseta percentage of PWM signal used for operating fans 68, and 70. The PWMsignal measurement and control functionality 122 of the dynamic tuningfirmware 100, includes the necessary instructions and/or computer codeto communicate with mother board 60 to electrically instruct fan controlmodule 67 to deliver a controlled PWM signal to fans 68, 70 forcontrolling the speed of fans 68, and 70. For example, as shown at 229,with a PWM preset value of 100%, mother board 60 instructs the fancontrol module 67 to deliver a PWM signal to fans 68, 70 to operate thefans at full speed regardless of the temperature of ASIC chips, and/orhash board. Although the PWM operator 229 depicts application of apercentage of PWM, it is appreciated that operator 229 may depict apercentage of RPM or an RPM value. Using only the fans 68, 70 to coolASIC chips and/or the hash boards is less effective, more challenging,and daunting where environmental temperatures are considered high orvary greatly.

Another method for cooling ASIC chips 66, 68, 70, 72, and/or hash boards62, 64 includes an auto-switch only cooling method where fans 68, 70 aredisabled, and the auto-switch mode is enabled for auto-tuning toselectively switch between chip profile configurations for dynamicallyadjusting chip voltages and chip frequencies provided in voltage andfrequency profile ranges associated with selected chip profileconfigurations, to find a target voltage that produces lower power usageto help reduce heat. Since target chip voltage correlates to powerusage, the higher the target chip voltage the higher the power usage,and the more heat generated, where the lower the target chip voltage thelower the power usage and the lower the heat generated by the ASICchips. When the auto-switch mode is enabled, and the measuredtemperature of the ASIC chips 66, 68, 70, 72, and/or hash boards 62, 64,fall outside acceptable temperature ranges, auto-switch selectivelyswitches between chip profile configurations and auto-tuning dynamicallyadjusts the chip voltage, and chip frequency of voltage and frequencyprofile ranges associated with the selected chip profile configuration,to find a target chip voltage that produces lower power usage at a givenfrequency range. In managing chip temperature for example, if duringmetric analysis it is determined that a measured chip temperature, ofone or more ASIC chips 66, 68, 70, 72 exceeds an upper chip temperaturevalue, provided at downscale if chip temp value higher 216 as shown inFIG. 5 , or if the measured chip temperature falls below a lower chiptemperature value, provided in upscale if chip temp value lower 218,auto-switch selectively switches from one chip profile configuration toanother chip profile configuration, and auto-tuning dynamically adjuststhe chip voltage, and frequency provided in the voltage and frequencyprofile range associated with the selected chip profile configuration tofind a target chip voltage that provides a lower power usage at a giventarget chip frequency to help reduce heat, while managing theperformance of ASIC chip 66, 68, 70, and 72. The auto-switch onlycooling method is repeated an x number of times to find a power usagevalue that maintains the temperature of ASIC chips within a safe,operating temperature range. In one embodiment, when switching betweenchip profile configurations, auto-switch changes from a chip profileconfiguration having a higher maximum chip voltage, and higher maximumchip frequency, to a chip profile configuration having a lower maximumchip voltage, and lower maximum chip frequency. On the other hand, ifthe ASIC chip temperature falls below a lower chip temperature value,provided in upscale if chip temp value lower 218, auto-switch switchesfrom a chip profile configuration having a lower maximum chip voltage,and lower maximum chip frequency, to a chip profile configuration havinga higher maximum chip voltage, and higher maximum chip frequency whereauto-tuning finds a target chip voltage, and target chip frequency thatproduces a higher hashrate. It is appreciated that when thecryptocurrency mining machines 16, 18, 20, nth operate in coolerenvironments, the lower temperatures allow users to increase target chipfrequency, or overclock the ASIC chips, to achieve a higher hashrate forincreasing profits while managing less heat.

In certain circumstances, either the fans 68, 70, or auto-switch modealone, is not enough to properly manage the temperature of ASIC chips66, 68, 70, 72, and/or hash boards 62, and 64. A more effective measureemploys use of both the fans 68, 70, and the auto-switch mode to moreaggressively control temperature and cool the ASIC chips. In referenceto controlling chip temperature for example, if during metric analysisthe measured temperature of one or more ASIC chips 66, 68, 70, 72exceeds an upper chip temperature value as provided at downscale if chiptemp value higher 216 in FIG. 5 , auto-switch selectively switches froma chip profile configuration including a voltage and frequency profilerange having a higher maximum chip voltage, and higher maximum chipfrequency, to a chip profile configuration including a voltage andfrequency profile range having a lower maximum chip voltage, and lowermaximum chip frequency where auto-tuning dynamically adjusts the lowermaximum chip voltage and lower maximum chip frequency, to find a targetchip voltage that provides lower power usage to help reduce heat. Uponswitching between chip profile configurations, the mother board 60,electrically communicates with fan control module 67 to graduallyincrease the PWM signal delivered to fans 68, 70 to increase therotational speed of fans 68, 70, and forcibly draw heat away from theASIC chip 66, 68, 70, and 72. In one example, auto-switch selectivelyswitches to another preset chip profile configuration when the speed ofthe fans 68, 70 are operating above a predetermined PWM percentagethreshold, and are unable, alone, to effectively reduce the temperatureof ASIC chip 66, 68, 70, and 72. A predetermined percentage threshold isprovided by users at 222 in FIG. 5 which corresponds to downscaleprofile if PWM is higher (switch to a chip profile configuration toprovide a lower target chip voltage if PWM signal is too high). Forexample, with a predetermined PWM percentage threshold set at 90%, whenthe speed of fans 68, 70 exceeds the 90% PWM threshold, auto-switchselectively switches to a chip profile configuration having lower chipvoltages for auto-tuning to find a target chip voltage that provideslower power usage to help reduce heat. With this approach, deference isgiven to the fans 68, 70 for cooling the temperature of ASIC chip, andauto-switch is employed when fans 68, 70 are operating above apredetermined PWM threshold, and are unable alone, to reduce thetemperature of ASIC chip 66, 68, 70 and 72. When switching to a lowerchip profile configuration, mother board 60 electrically communicateswith the fan control module 67 to retain the speed of fans 68, 70 or togradually decrease the PWM signal delivered to fans 68, 70 whichdecreases the rotational speed of the fans 68, 70 as a result of thelower power usage producing less heat.

However, if during metric analysis on-chip sensors 86, 88, 90, 92measure the temperature of one or more ASIC chips 66, 68, 70, 72, andthe measured chip temperature falls below a preset chip temperaturevalue provided in upscale if chip temp value lower 218, auto-switchselectively switches from a chip profile configuration including avoltage and frequency profile range having a lower maximum chip voltage,and lower maximum chip frequency, to a chip profile configurationincluding a voltage and frequency profile range having a higher maximumchip voltage, and higher maximum chip frequency where newly determinedtarget chip voltage, and target chip frequency associated with theselected chip profile configuration, is applied to ASIC chip 66, 68, 70,72 to optimize the performance of the ASIC chip by increasing thehashrate while managing power usage and chip temperature. It isappreciated that efforts to cool ASIC chips, and/or hash boards are lessimperative when cryptocurrency mining machines operate in coolerenvironmental temperatures. Upon switching between chip profileconfigurations, the mother board 60, communicates with fan controlmodule 67 to decrease, maintain and/or gradually increase the PWM signaldelivered to fans 68, 70 to increase the rotational speed of fans 68, 70to forcibly draw heat away from the ASIC chip 66, 68, 70, 72 as theincreased hashrate and power usage produces and increase in chiptemperature over time. In one example, auto-switch selectively switchesto another preset chip profile configuration when the speed of fans 68,70 operates below a PWM percentage threshold as provided by users at 224in FIG. 5 which corresponds to upscale profile if PWM is lower. Forexample, with a predetermined PWM percentage threshold set at 50%, whenthe speed of the fans 68, 70 goes lower than the preset 50% PWMthreshold, auto-switch selectively switches to a higher chip profileconfiguration (upscale profile) where auto-tuning finds a target chipvoltage and frequency that provides higher power usage to accommodate ahigher hashrate. In this approach, when switching to a higher chipprofile configuration, mother board 60 electrically communicates withthe fan control module 67 to gradually increase the PWM signal overtime,to increase the rotational speed of the fans 68, 70 as a result of thehigher power producing more heat over time. Enabling both operation ofauto-switch and fans 68, 70 provides greater cooling of ASIC chips 66,68, 70, 72, and/or hash boards 62, and 64. It is appreciated that themethods described herein for cooling ASIC chips, is also applicable tomanaging the temperature of has boards 62, 64, via sensors 78, 80, andfans 68 and 70.

As illustrated in FIG. 5 , the dynamic tuning firmware 100 also providesa chain disable temp feature 225 where the cryptocurrency mining machine16, 18, 20, nth will automatically turn off when the temperature of theASIC chips, and/or hash boards exceeds a preset temperature value. Thecryptocurrency mining machine may be manually rebooted, or automaticallyrebooted after a predefined period of time. It is appreciated that anadditional disable temp feature can be provided for the cryptocurrencymining machines to automatically turn off when the internal and/orenvironmental temperature exceeds a preset temperature value.

Cryptocurrency miners 16, 18, 20, nth, and more specifically, ASIC chips66, 68, 70, 72, require large amounts of processing power to solveinherently difficult algorithms when mining digital currency. Largerpower demands result in higher operating costs, so mining digitalcurrency, such as bitcoins, can be an expensive endeavor. To attainprofitable margins while overcoming operating costs, cryptocurrencymining machines 16, 18, 20, nth must operate effectively and efficientlywhen mining digital currency. As such, optimizing the performance ofASIC chips 66, 68, 70, 72, to promote efficient operation ofcryptocurrency mining machines 16, 18, 20, nth, is also based on acondition parameter including profit. A profit analysis process 124 ofthe dynamic tuning firmware 100 provides a profit analysis phase inwhich a profit algorithm is applied to tuned chip parameters todetermine what tuned chip parameters provide the highest profits whenapplied to ASIC chips for mining a particular type of digital currency.In practice, auto-tuning dynamically adjusts the chip voltage and chipfrequency, of voltage and frequency profile ranges associated with eachchip profile configuration, to provide tuned chip parameters thatinclude a target chip voltage and target chip frequency which providethe lowest power usage at the highest hashrate. The tuned chipparameters includes actual tuned chip parameters that are determinedfrom selected chip profile configurations in real-time. However, in onealternative embodiment, the tuned chip parameters may include estimatedtuned chip parameters that includes target chip voltages and target chipfrequencies that are determined from the average or mean values ofhistorical target chip voltages, and chip frequencies used in previousmining operations, or calculated target chip voltages, and target chipfrequencies based on power usage, and hashrate values. Tuned chipparameters are stored in tuned data management files, data indexes, orin look-up tables stored on each control unit 14, 16, in memory ofmother board 60, in database 24, and/or on server 26. When mining aparticular digital currency such as bitcoin, the profit analysis phaseapplies a profit algorithm to the tuned parameters to determine whattuned parameters provide the lowest power and highest hashrate to garnerthe highest profit when mining bitcoin.

The profit analysis phase begins by pre-configuring profit variablesused in the profit algorithm process. The tuning preset configurationblock 102 of the dynamic tuning firmware 100 provides a profitabilityconfiguration interface 110 which generates a profitabilityconfiguration screen 500, as shown in FIG. 9 . A profitabilitysub-function tab 502, associated with the miner configuration tab 204,is activated to access the profitability configuration screen 500 topreconfigure profit variables used during the profit analysis phase fordetermining profits. Two such profit variables include current price ofdigital currency mined (coin price), and difficulty (D). Both currentcoin price, and difficulty are generally managed by a third party, via,a digital mining currency exchange network 28, shown in FIG. 1 . Thecoin price may be established in two different ways. For example,enabling a coin price operator 504 allows each cryptocurrency miningmachine 16, 18, 20, nth via, the dynamic tuning firmware, toautomatically retrieve the current coin price from the digital currencyexchange network, via server 26 over communication network 22. It isnoted that current coin prices can be retrieved automatically every xseconds, minutes, hours, or days, or an X number of times, or on aparticular day and time. Alternatively, users can manually enter thecoin price in a designated entry box 506, if known by users in advance.Profit variable difficulty (D) is a measure of how difficult it is tofind a correct hash (solve a complex cryptocurrency mathematical puzzle)when mining digital currency. For example, a bitcoin block is added to ablock chain every 10 minutes, and difficulty is adjusted periodicallyover time, (i.e. is increased or decreased) to manage the time in whichthe bitcoin block is added to the block chain depending on variousfactors including for example, the number of miners on the network, andthe combined hash power used. The digital mining network protocolautomatically adjusts difficulty after 2016 blocks have been mined inthe network. Difficulty is based on the ease at which bitcoins aremined. For example, when it is easy to mine bitcoins, difficulty isincreased, and when it is harder to mine bitcoins difficulty isdecreased. As such, difficulty must be taken into consideration whenascertaining profits. As shown in FIG. 9 , there is provided a coindifficulty operator 508 for automatically retrieving difficulty from thedigital currency network exchange 28. Alternatively, the coin difficultyoperator 508 may be disabled, and difficulty can be manually enteredvia, designated entry box 510, if known by users.

Additional profit variables used in the profit analysis phase, includesthe price of electricity 512, a profit interval 514, and a profitthreshold 516. The profit interval 514 may include an x number of times,or a specific time that the profit phase is initiated to determine whichtuned parameters provide the highest profits. For example, a profitinterval of 12 hrs, or a profit interval set at 9 a.m. means the profitalgorithm phase occurs every 12 hrs. or at 9 a.m. every day. Asillustrated in FIG. 9 , users can manually enter a profit threshold in adesignated query box 516. The profit threshold 516 may include apercentage of calculated profits, or alternatively, a fixed numericalvalue, or any combination thereof. For example, if calculated profitfalls below a chip profit threshold, auto-switch selectively switchesbetween tuned chip parameters to find tuned chip parameters (i.e. atarget chip voltage and target chip frequency) that provides the lowestpower usage, and the highest hashrate to garner a profit that is equalto, or greater than, the chip profit threshold value, and appliesselected tuned parameters to the ASIC chips to mine digital currency. Inone embodiment, the profitability configuration screen 500 may provide adrop down menu (not shown) that provides a plurality of selectablepredetermined profit threshold values. The profit analysis may also takeinto account the amount of change in the profit or the amount ofdeparture from the profit threshold. For example, if the profit changeis minimal, it may not be economically advantageous to switch parametersbased on chip down time while the parameters are being switched. Inother words, there may be a predetermined profitability change thresholdthat must be exceed before it becomes economically advantageous tochange the parameters.

Certain costs and fees associated with mining digital currency areincluded as profit variables when determining net profit. The price ofelectricity for operating cryptocurrency mining machines 16, 18, 20, andnth impacts profit margin. As shown in FIG. 9 , the price of electricityis manually entered by users at 512, or alternatively, eachcryptocurrency mining machine 16, 18, 20, nth can include circuitry toautomatically retrieve the price of electricity from an electrical powergrid via, communication network 22. Thus, in an exemplary embodiment,calculating the estimated profitability of a mining chip may includecalculating an estimated power consumption of the mining chip (based onvoltage and frequency in one embodiment), and using this estimated powerconsumption along with the electricity price to further calculate theestimated profitability. Cost may also include operational costsattributed to maintenance, repairs, parts, labor, transport, networkservices, storage, lighting, cooling, shelter, and loss profits. Theaccumulated value of operational costs is entered by users at 518. Feesassociated with operating cryptocurrency mining machines 16, 18, 20,nth, may include fees for miner pools, network services, rental,storage, or transport, firmware, exchanging or selling digital currency,license, contract, lease, or royalty. The accumulated value of fees isentered by users at 520. Costs and fees associated in operating eachcryptocurrency mining machine 16, 18, 20, nth may be itemized,categorized, or classified in cost and fee data management files,indexes, tables, that are stored in memory on mother board 60, indatabase 24, on server 26, and/or on computing device 12, 14, or anycombination thereof.

Another profit variable considered includes power consumption. As thestandard of difficulty D, and/or hashrate increases, so does the powerconsumed by the ASIC chip 66, 68, 70, 72 to continuously process miningalgorithms. As shown in FIG. 9 , a power usage operator 522 isselectively enabled by users for each cryptocurrency mining machine toretrieve the power usage value of each machine for use as a profitvariable in the profit algorithm. Power usage of each individualcryptocurrency mining machine is defined as power consumed by allelectronic components including I/O interface network 36, control board60, fans 68, 70, power supply 66, sensors 78, 80, 82, 84, 86, 88, 90,92, and ASIC chip 66, 68, 70, 72, or alternatively, as power consumedonly by the ASIC chips 66, 68, 70, and 72. Power usage is eithercalculated by considering the target chip voltage, and target chipfrequency of each chip profile configuration, or alternatively, powerusage is actually measured in real-time via, electrical power measuringcircuitry or modules provided in each cryptocurrency mining machine 16,18, 20, and nth, or by a power grid. Either the calculated, or measuredpower usage is employed to compute profits when mining digital currency.In one alternative embodiment, power usage is manually entered by usersif known, via, a designated entry box 524. In another embodiment, uponenabling the power usage operator 522, each cryptocurrency miningmachine 16, 18, 20, nth electrically communicates with an electricalpower grid via, communication network 22, to automatically retrieve thepower usage value from the electrical power grid which is automaticallystored on the mother board 60 for use in the profit algorithm.

Still another profit variable used in determining profits includes thehashrate (H) of ASIC chip 66, 68, 70, and 72. Typically, as hashrateincreases, so does profit because ASIC chips are solving complexmathematical equations at greater speeds which is why it is important tofind the target chip frequency that provides for the highest hashrate.The hashrate of each ASIC chip can be determined in real-time, orcalculated based on the number of hash cores. Other profit variables toconsider include number of days N spent mining for digital currency, anda reward per block chain B provided by the digital currency networkexchange 28 protocols.

During a profit analysis phase, a profit algorithm is applied to each ofthe tuned chip parameters (i.e. the target chip voltages, and targetchip frequencies associated with each chip profile configurations). Theprofit algorithm calculates profit based on power usage associated witheach target chip voltage, and hashrate associated with target chipfrequency. The profit algorithm is formulated to determine gross, or netprofits. In one example there is provided a net profit algorithm definedas: (((N×B×H×S/D×2³²)×coin price)−(power usage×price ofelectricity)−costs−fees), where N is the number of mining days; B is thereward per block, H is the hashrate (hashes per second) as determined bythe target chip frequency; S is the number of seconds per day; D isdifficulty, coin price is current price of digital currency or coinbeing mined, power usage is the retrieved, measured, or calculated powerusage associated with the target chip voltage, price of electricity isestablished by the third-party electrical company, and costs and feesare associated with operating cryptocurrency mining machines.

A few representative examples are provided to explain applicability ofthe profit algorithm to tuned chip parameters associated with selectedchip profile configurations. It is noted that for exemplary purposesonly, the cost of electricity, power usage, and mining pool fee are theonly costs, and fees considered in the examples. Additional operationscosts, and fees are generally applied when determining net profits. Eachexample is directed to a single cryptocurrency mining machine 16 that ismining digital currency including bitcoin. It is understood that theexample may be directed to mining other types of digital currency.

First Example Exemplary Profit Variables

Coin Price Enabled—ON ($59,000.00, automatically retrieved from digitalexchange)

Manual Coin Price—N/A

Difficulty Enabled—ON (D=23,137,439,666,472)

Manual Coin Difficulty—N/A

Price of Electricity—$0.10 kWh

Profit interval—12 hours

Profit Threshold—5%

Operational costs—$0

Fees—4%

Power Usage—ON

Manual Power Usage—N/A

H=25 TH/s (hashrate of ASIC chips at one chip profile configuration)

N=1 day (default to 30 days of mining)

B=6.25

S=86620 (number of seconds per day)

In a first example, tuned chip parameters associated with a selectedchip profile configuration include a target chip voltage of 17 volts,and a target chip frequency of 660 MHz, which yields a chip hashrate of25 TH/s based on the target chip frequency of 660 MHz, and a calculatedpower usage of 1000 watts (1 kw) based on a target chip voltage of 17volts. It is noted that the hashrate, and power usage includes anaverage or mean value of all ASIC chips 66, 68, 70, 72 provided in adesignated cryptocurrency mining machine 16. In contemplating profitvariables set at: N=1 day, B=6.25, H=25 th/s; S=86620, andD=23,137,439,666,472.00, the cryptocurrency mining machine 16 mines.000135 bitcoins a day (1×6.25×25×86620)/ 23,137,439,666,472×2³²). Thegross profit for mining .000135 bitcoins in one day is $7.965 per day(i.e. bitcoin price $59,000.00×.000135). A power usage of 24 kWh (1kWh×24 hrs. of mining) at a rate of 10 cents per kilowatt hr results ina cost of $2.42 a day. Fees such as mining pool fees of 4% yields $0.097a day. As such, in accordance with tuned chip parameters having a targetchip voltage of 17 volts, and target chip frequency of 660 MHz, thecryptocurrency mining machine 16 has made a net profit of $5.448 perday. ($7.965−$2.42−$.097). Given a hashrate of 25 TH/s at 1000 watts ofpower usage, the efficiency of the cryptocurrency mining machine 16 is40 watts/th. Thus, cryptocurrency mining machine 16 consumes 62 watts ofpower for every 1 th/s of hashing power.

Attributing the first example to a preconfigured chip profile threshold,when the calculated net profit falls below the profit threshold providedby users at 516 in FIG. 9 , the cryptocurrency mining machine 16continues to selectively switch between different tuned chip parameterswhere the profit analysis phase applies the profit algorithm to each ofthe selected tuned parameters to determine which of the tuned parametersprovide a net profit that is equal to, or higher than, the chip profitthreshold value. Thus, given an exemplary profit threshold of 5%, ifupon initiating the profit analysis phase it is determined that the netprofits attributed to selected tuned parameters falls below $5.266($5.533−($5.533×.05)), the cryptocurrency mining machine 16 willselectively switch between different tuned parameters until a calculatednet profit of a selected tuned parameters reaches, or exceeds a profitof $5.266. Per the profit interval 414, the profit analysis phaseapplies the profit algorithm to each of the tuned parameters todetermine net profit every 12 hours when mining for bitcoin.

There is provided a second example of calculating net profit fordifferent tuned parameters associated with another selected chip profileconfiguration that provides higher hashrate, and power usage values.

Second Example Exemplary Profit Variables

Coin Price Enabled—ON ($59,000.00, automatically retrieved from digitalexchange)

Manual Coin Price—N/A

Difficulty Enabled—ON (D=23,137,439,666,472)

Manual Coin Difficulty−N/A

Price of Electricity—$0.10 kWh

Profit interval—12 hours

Profit Threshold—5%

Operational costs—$0

Fees—4%

Power Usage—ON

Manual Power Usage—N/A

H=66 TH/s (hashrate of ASIC chips at another chip profile configuration)

N=1 day (default to 30 days of mining)

B=6.25, S=86620

In this example, tuned chip parameters associated with another selectedchip profile configuration includes a target chip voltage of 19 volts,and a target chip frequency of 700 MHz, yielding a hashrate at 66 TH/s,and a calculated power usage of 3000 watts (3 kW). In contemplatingprofit variables set at: N=1 day, B=6.25, H=66 TH/s; S=86620 sec, andD=23,137,439,666,472.00, the cryptocurrency mining machine 16 mines.00027 bitcoins a day (1×6.25×66×86620)/23,137,439,666,472×2³²). Thegross profit for mining .00027 bitcoins in one day is $15.93 per day(i.e. bitcoin price $59,000.00×.00027). Considering power usage of 72kWh (3 kwh×24 hrs.) at a rate of 10 cents per kWh results in $7.20 aday, and fees associated with mining pool fees of 4% yields $0.288 Assuch, with a hashrate of 66 TH/s, cryptocurrency mining machine 16 hasmade a net profit of $8.442 per day ($15.93−$7.20−$0.288). Given ahashrate of 66 TH/s, and 3000 watts of power usage, the mining machineis operating at an efficiency of 45.45 watts/th. When cryptocurrencymining machine 16 utilizes the newly selected tuned parameters, themining machine consumes 60 watts of power for every 1 th/s of hashingpower. As shown, an increase in both hashrate, and power usage, pursuantto newly selected tuned chip parameters, has generated higher profits inone day when compared to the profit of the first tuned parameters in thefirst example.

A third example is provided to illustrate calculating net profit fordifferently selected tuned parameters which include a target chipvoltage, and target chip frequency that provide for a higher powerusage, and hashrate.

Third Example Exemplary Profit Variables

Coin Price Enabled—ON ($59,000.00, automatically retrieved from digitalexchange

Manual Coin Price—N/A

Difficulty Enabled—ON (D=23,137,439,666,472)

Manual Coin Difficulty—N/A

Price of Electricity—$0.10 kWh

Profit interval—12. hours

Profit Threshold—5%

Operational costs—$0

Fees—4%

Power Usage—ON

Manual Power Usage—N/A

H=60 TH/s (hashrate of ASIC chips at another chip profile configuration)

N=1day (default to 30 days of mining)

B=6.25

S=86620 (number of seconds per day)

In the third example, tuned chip parameters associated with anotherselected chip profile configuration includes a target chip voltage of 18volts, and a target chip frequency of 800 MHz, yielding a chip hashrateat 60 TH/s, and a calculated power usage of 8000 watts (8 kW). Anincrease in power usage, and hashrate correlates to an increase in bothtarget chip voltage and target chip frequency determined from theselected chip profile configuration. With profit variables set at thefollowing: N=1 day, B=6.25, H=60 TH/s; S=86620 sec, andD=23,137,439,666,472.00, the cryptocurrency mining machine 16 mines.000324 bitcoins a day (1×6.25×60 x 86620)/23,137,439,666,472×2³²). Thegross profit for mining 0.000324 bitcoins in one day is $19.234 per day(i.e. bitcoin price $59,000.00×.000324). Considering power usage of 192kWh (8 kwh×24 hrs.) at a rate of 10 cents per kWh results in a cost of$19.20 a day, and fees such as mining pool fees of 4% yields $0.768.Given a hashrate of 60 TH/s, cryptocurrency mining machine 16 has made anet profit of ($19.234−$19.20−$.768)=−$0.734. A hashrate of 60 TH/s, at8000 watts provides an operating efficiency of 133 watts/th. When thecryptocurrency mining machine 16 operates with the selected tuneparameters as provided, the mining machine consumes 133 watts of powerfor every 1 th/s of hashing power. As shown in the third example, afurther increase in power usage, and hashrate actually provides a lowernet profit for the same digital coin mined at the same coin price, anddifficulty with cryptocurrency mining machine 16 operating at the sameprice of electricity, and operating costs, and fees. As shown, theoperating efficiency of the cryptocurrency mining machine 16 has greatlydecreased as a result of the selected tuned chip parameters. Uponcompletion of the profit analysis phase, the cryptocurrency miningmachine 16 is automatically tuned by selecting the tuned chip parametersprovided in the second example, and the selected tuned chip parametersare applied to the ASIC chips to garner the highest profit when miningbitcoin. It is appreciated that tuned chip parameters which provide thehighest profits are learned from the machine learningmodule/models/algorithms, and stored in a designated data table, dataindex, data file, and/or look-up table that is stored in memory onmother board 60, storage 52 of each computing device 12, 14, on database24, and/or on server 26. Each cryptocurrency mining machine 16, 18, 20,nth may retrieve any of the tuned chip parameters, and directly applythe retrieved tuned chip parameters to the ASIC chips 66, 68, 70, 72. Inone embodiment, auto-tuning may be performed using any of the retrievedtuned chip parameters to determine new target chip voltages, and targetchip frequencies that provide the lowest power usage, and highest hashrate, and then initiate the profit analysis phase on the newlyestablished tuned chip parameters. Thus, previously tuned parameters maybe auto-tuned further to provide refined target chip voltage and targetchip frequency.

As shown in the examples given, changes in profit variables correspondto changes in profit. An increase in hashrate, or coin price, and adecrease in difficulty, costs, fees, price of electricity, or powerusage all result in higher profits. Each of the tuned chip parametersdetermined from selected chip profile configurations, provide differentpower usage, and hashrate values, respectively, which influence profits.Although increases in target chip frequency provide higher hashrates,increases in target chip voltages result in higher power usage whichleads to diminishing returns in profit, an increase in chip temperature,and a reduction in operating efficiency of cryptocurrency miningmachines 16, 18, 20, and nth. As illustrated in FIG. 10 , an increase inboth target chip voltage, and target chip frequency results in anincrease in power usage, and hashrate, respectively, while the operatingefficiency of cryptocurrency mining machine decreases as watts/thincreases. An increase in hashrate generally corresponds to an increasein bitcoins, but an increase in power usage results in higher operatingcosts. As power usage rises, the operating efficiency of thecryptocurrency mining machines drop because the watts per th/sincreases. As such, an increase in hashrate does not necessarily resultin an increase in profits, because power usage also increases. Inreviewing the three examples given, the cryptocurrency mining machine 16operating at a target voltage of 18 volts, and target frequency of 800MHz, as determined from the profit analysis phase, provides the mostprofit at $8.09 a day.

Each cryptocurrency mining machine 16, 18, 20, nth is rebooted to applythe tuned chip parameters associated with the chip profile configurationto the ASIC chips 66, 68, 70 and 72. Rebooting the cryptocurrency miningmachines 16, 18, 20 nth is required to prevent the mining machines fromcrashing, to prevent chip instability, or to prevent loss ofcommunication with ASIC chips. The need to constantly rebootcryptocurrency mining machines for applying target chip voltages andfrequencies to ASIC chips can be costly, time consuming, and taxing onthe mining machines, not to mention the down time of the miningmachines. To overcome the need for rebooting, there is provided a chipsetting process (CSP) 134 of the dynamic tuning firmware 100. During thechip setting process, each previously applied target chip voltage, andtarget chip frequency is dynamically adjusted in small increment ordecrement values over a predefined period of time until arriving at thenewly determined target chip voltage and target chip frequencyassociated with the newly selected chip profile configuration, or tunedchip parameters. In other words, in an exemplary embodiment, the voltageand/or frequency of the chip may be adjusted while maintaining themining chip in a mining state, i.e., without having to restart the chip.The chip set algorithm may define an increment or decrement voltage andfrequency value, and the rate at which the increment and decrementvoltage and frequency values are applied. For example, the increment ordecrement voltage values may include 0.1 volt to 1 volt, and incrementor decrement frequency values may include 2 MHz to 50 MHz. The incrementor decrement frequency may be applied in an integral, linear,derivative, stepped, exponential, or progressive manner, or anycombination thereof. The rate at which the previous target chip voltage,and target chip frequency changes may include anywhere from 0.1milliseconds to 3 seconds. In applying the chip set algorithm, theprevious tuned parameters are adjusted over a predetermined of time,until arriving at the target chip voltage, and target chip frequency, ofthe newly applied chip profile configuration, or tuned chip parameters.When selectively switching from a target chip voltage, and target chipfrequency having a high value, to a newly determined target chip voltageand target chip frequency having a lower value, auto-tuning, via thechip setting process, dynamically decreases previous target chipfrequency first, until arriving at the new lower target chip frequency,and then subsequently decreases the higher target chip voltage untilarriving at the new target chip voltage for managing power usage, andhashrate based on temperature and/or profit. Thus, the previous targetchip frequency is adjusted first, and then the previous target chipvoltage second. However, when selectively switching from a target chipvoltage, and target chip frequency having a lower value to a target chipvoltage, and target chip frequency having a higher value, auto-tuning,via the chip setting process, dynamically increases the previous lowertarget chip voltage first, via an increment or decrement voltage value,until reaching the new higher target chip voltage, and then subsequentlyincreases the lower target chip frequency, via an increment or decrementfrequency value, until reaching the new higher target chip frequency ofthe newly tuned parameters. In this scenario, the target chip voltage isadjusted first, and then the target chip frequency. It is appreciatedthat the chip setting process may be enabled or disabled by users. Ifthe chip setting process is disabled by users, cryptocurrency miningmachine 16, 18, 20, nth will automatically reboot each time, to applynewly determined target chip voltages, and target chip frequenciesdetermined during auto-tuning, or newly determined tuned parametersdetermined during the profit analysis phase.

During auto-tuning, each cryptocurrency mining machine 16, 18, 20, nthprocesses a plurality of chip profile configurations to determine aplurality of tuned chip parameters each including a target chip voltage,and a target chip frequency for managing power usage and hashrate basedon temperature. When auto-tuning initiates a profit analysis phase, aprofit algorithm is applied to each tuned chip parameter to find whichtuned chip parameters provide the highest profits, and applies the tunedchip parameters to the ASIC chips to garner the highest profit. Whenmining digital currency over time, the operating conditions andcharacteristics of the cryptocurrency mining machines, and moreparticularly of the ASIC chips 66, 68, 70, 72, generally become moredefined, predictable, established, and known. Applying tuned chipparameters associated with chip profile configurations to ASIC chips 66,68, 70, 72 based on predictive, defined, and reliable operatingconditions, performance characteristics, and condition parameters,enhances the effective management, and operation of cryptocurrencymining machines 16, 18, 20, nth by reducing the time needed forauto-tuning, the ability to manage chip temperature more effectively,ability to enhance profits by reducing down time, reducing wear and tearon the equipment, and more importantly, increasing the efficiency ofcryptocurrency mining machines when mining digital currency. Tocapitalize on such benefits, there is provided in one embodiment, ahierarchical storage manager for applying tuned chip parameters to ASICchips 66, 68, 70, 72 in a more time saving, and cost effective manner.The hierarchical storage manager is governed by the hierarchical storagemanager control process 132 of the dynamic tuning firmware 100.Previously tuned or learned chip parameters including target chipvoltages, and target chip frequencies known to optimize the performanceof ASIC chips can be classified in various hierarchical groups, orsub-groups according to predefined weights, rules or policies. For orexample, tuned chip parameters that are known to maintain chiptemperature within a given temperature range, under certain operatingconditions, may be classified in a first hierarchical group. A ruleassociated with the first hierarchical group may define a particulartemperature range for given target chip voltages, and target chipfrequencies. For example, a target chip voltage, and target chipfrequency that provides a certain power usage, and hashrate value at afirst chip temperature range may be classified in a first hierarchicalgroup, while a target chip voltage, and target chip frequency thatprovides a certain power usage, and hashrate value at another chiptemperature range, may be classified in a second hierarchical group.Alternatively, tuned chip parameters may be classified in certainhierarchical groups based on profit values, or profit thresholds Tunedchip parameters that are applied less frequently, or increase chiptemperatures, or provide less favorable profits may be classified inlower hierarchical groups. Each cryptocurrency mining machine 16, 18,20, nth may retrieve the tuned chip parameters from any hierarchicalgroup in accordance with predicated operating conditions, and/orcondition parameters, and apply the retrieved tuned parameters to ASICchips 66, 68, 70, 72 based on measured, calculated, or predictedoperating conditions, or condition parameters. The hierarchical storagemanager may be stored on computing devices 12, 14 as shown in FIG. 2 ,and accessible by each cryptocurrency mining machine, via communicationnetwork 22, or alternatively, the hierarchical may be stored in memoryof the mother board 60 of each cryptocurrency mining machine 16, 18, 20,and nth. The hierarchical storage manager can operate in conjunctionwith, or in addition to, machine learning modules/models/algorithms.Some benefits of using a hierarchical storage manager include: 1).support for rule or policy-based organization of tuned chip parameters,2). based on inputs provided by each cryptocurrency mining machine, oron condition parameters, or profit variables, the hierarchical storagemanager can build its own rules and policies, and apply them todifferent settings, 3). hierarchical storage manager can increase theefficiency and performance of cryptocurrency mining machines by applyingalready tuned parameters known to work efficiently on predicted or knowncondition parameters, 4). can increase the speed of auto-tuning byclassifying tuned chip parameters used less frequent in lowerhierarchical groups, and 4). hierarchical storage manager canautomatically perform on-line data backups of tuned parameters, duringpower outage, or loss of Internet connection.

There may be circumstances where tuned chip parameters applied to ASICchips to garner higher profits pose an issue regarding chip temperature.Thus there are times when it may be necessary for auto-tuningcryptocurrency mining machines 16, 18, 20, nth based on both temperatureand profit. For example, assuming a profit interval is initiated, andone or more profit variables have changed so that when the profitanalysis phase applies the profit algorithm to tuned parameters,auto-tuning determines that new tuned parameters including a highertarget chip voltage, and higher target chip frequency provide for higherprofits, and applies the new tuned parameters to the ASIC chips.However, due to a higher target chip voltage of the new tunedparameters, the temperature of ASIC chips also increases above apreconfigured downscale if chip temperature is higher as provided at 216in FIG. 5 when the higher target chip voltage is applied to the ASICchips. Because of the increase in temperature, auto-switch selectivelyswitches to other tuned parameters associated with chip profileconfigurations for auto-tuning to find a target chip voltage, and targetchip frequency that provides lower power consumption or usage to helpreduce the heat. The preset chip profile configuration, associated withthe tuned parameters that resulted in the ASIC chips overheating, isdisabled for a set period of time and cannot be used in auto-switch andauto-tuning mode. Also, all preset chip profile configurations thatinclude tuned parameters having higher target chip voltage values whichwould increase chip temperatures above acceptable temperature ranges arealso disabled for a set period of time.

Since many modifications, variations, and changes in detail can be madeto the described embodiments, it is intended that all matters in theforegoing description and shown in the accompanying drawings beinterpreted as illustrative and not in a limiting sense. Furthermore, itis understood that any of the features presented in the embodiments maybe integrated into any of the other embodiments unless explicitly statedotherwise. The full scope of the claims should be determined by both theappended claims and their legal equivalents.

This disclosure, in various embodiments, configurations and aspects,includes components, methods, processes, systems, and/or apparatuses asdepicted and described herein, including various embodiments,sub-combinations, and subsets thereof. This disclosure contemplates, invarious embodiments, configurations and aspects, the actual or optionaluse or inclusion of, e.g., components or processes as may be well-knownor understood in the art and consistent with this disclosure though notdepicted and/or described herein.

The phrases “at least one”, “one or more”, and “and/or” are open-endedexpressions that are both conjunctive and disjunctive in operation. Forexample, each of the expressions “at least one of A, B and C”, “at leastone of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B,or C” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.

In this specification and the claims that follow, reference will be madeto a number of terms that have the following meanings. The terms “a” (or“an”) and “the” refer to one or more of that entity, thereby includingplural referents unless the context clearly dictates otherwise. As such,the terms “a” (or “an”), “one or more” and “at least one” can be usedinterchangeably herein. Furthermore, references to “one embodiment”,“some embodiments”, “an embodiment” and the like are not intended to beinterpreted as excluding the existence of additional embodiments thatalso incorporate the recited features. Approximating language, as usedherein throughout the specification and claims, may be applied to modifyany quantitative representation that could permissibly vary withoutresulting in a change in the basic function to which it is related.Accordingly, a value modified by a term such as “about” is not to belimited to the precise value specified. In some instances, theapproximating language may correspond to the precision of an instrumentfor measuring the value. Terms such as “first,” “second,” “upper,”“lower” etc. are used to identify one element from another, and unlessotherwise specified are not meant to refer to a particular order ornumber of elements.

As used herein, the terms “may” and “may be” indicate a possibility ofan occurrence within a set of circumstances; a possession of a specifiedproperty, characteristic or function; and/or qualify another verb byexpressing one or more of an ability, capability, or possibilityassociated with the qualified verb. Accordingly, usage of “may” and “maybe” indicates that a modified term is apparently appropriate, capable,or suitable for an indicated capacity, function, or usage, while takinginto account that in some circumstances the modified term may sometimesnot be appropriate, capable, or suitable. For example, in somecircumstances an event or capacity can be expected, while in othercircumstances the event or capacity cannot occur—this distinction iscaptured by the terms “may” and “may be.”

As used in the claims, the word “comprises” and its grammatical variantslogically also subtend and include phrases of varying and differingextent such as for example, but not limited thereto, “consistingessentially of” and “consisting of ” Where necessary, ranges have beensupplied, and those ranges are inclusive of all sub-ranges therebetween.It is to be expected that the appended claims should cover variations inthe ranges except where this disclosure makes clear the use of aparticular range in certain embodiments.

The terms “determine”, “calculate” and “compute,” and variationsthereof, as used herein, are used interchangeably and include any typeof methodology, process, mathematical operation or technique.

Reference to a “detonator holder and/or detonator” herein refers to atleast one of a detonator holder and a detonator, and may be termed adetonation-related element for more convenient reference.

This disclosure is presented for purposes of illustration anddescription. This disclosure is not limited to the form or formsdisclosed herein. In the Detailed Description of this disclosure, forexample, various features of some exemplary embodiments are groupedtogether to representatively describe those and other contemplatedembodiments, configurations, and aspects, to the extent that includingin this disclosure a description of every potential embodiment, variant,and combination of features is not feasible. Thus, the features of thedisclosed embodiments, configurations, and aspects may be combined inalternate embodiments, configurations, and aspects not expresslydiscussed above. For example, the features recited in the followingclaims lie in less than all features of a single disclosed embodiment,configuration, or aspect. Thus, the following claims are herebyincorporated into this Detailed Description, with each claim standing onits own as a separate embodiment of this disclosure.

Advances in science and technology may provide variations that are notnecessarily express in the terminology of this disclosure although theclaims would not necessarily exclude these variations.

What is claimed is:
 1. A non-transitory computer-readable medium storingthereon computer-executable instructions that, when executed by aprocessor in communication with a mining machine including a pluralityof hash boards each including a plurality of mining chips, cause theprocessor to perform: establishing communication with an external devicevia an external network; retrieving at least one profit variable fromthe external device via the external network; calculating an estimatedprofitability of at least a first mining chip of the plurality of miningchips as a function of at least a hashrate of at least the first miningchip, a power consumption of at least the first mining chip, and the atleast one profit variable; and adjusting a chip voltage supplied to atleast the first mining chip and adjusting a chip frequency of at leastthe first mining chip to maximize the estimated profitability; wherein:the power consumption of at least the first mining chip is calculatedbased on at least two of the chip voltage, the chip frequency, and thetemperature of at least the first mining chip.
 2. The non-transitorycomputer-readable medium of claim 1, wherein the at least one profitvariable further comprises at least one of a block reward value, adigital coin price, electricity price, and a difficulty value.
 3. Thenon-transitory computer-readable medium of claim 1, wherein the at leastone profit variable is retrieved at a predetermined time interval. 4.The non-transitory computer-readable medium of claim 1, thecomputer-executable instructions further causing the computer toperform: measuring a temperature of at least the first mining chip or atemperature of at least a first hash board of the plurality of hashboards using a temperature sensor; and adjusting the chip voltage andthe chip frequency in response to the temperature being outside of apredetermined temperature range.
 5. The non-transitory computer-readablemedium of claim 1, the computer-executable instructions further causingthe computer to perform: storing a plurality of chip profileconfigurations in a memory; wherein each chip profile configuration ofthe plurality of chip profile configurations comprises at least oneparameter for configuring at least the first mining chip.
 6. Thenon-transitory computer-readable medium of claim 5, wherein the at leastone parameter is at least one of a power consumption, a voltage rangefor at least the first mining chip, a frequency range of at least thefirst mining chip, a voltage adjustment value, and a tuning cycle value.7. The non-transitory computer-readable medium of claim 6, wherein: theat least one parameter comprises a voltage range for at least the firstmining chip; and the adjusting a chip voltage supplied to the miningchip comprises dynamically adjusting the chip voltage within the voltagerange to identify a target chip voltage which is a minimum voltage atwhich a hashrate of at least the first mining chip is at or above apredetermined hashrate threshold.
 8. The non-transitorycomputer-readable medium of claim 6, wherein: the at least one parametercomprises a frequency range for at least the first mining chip; and theadjusting a chip frequency of at least the mining chip comprisesdynamically adjusting the chip frequency within the frequency range toidentify a target chip frequency at which the hashrate of at least thefirst mining chip is closest to an ideal hashrate.
 9. The non-transitorycomputer-readable medium of claim 5, the computer-executableinstructions further causing the computer to perform: receiving an inputfrom a user to set an input value of the at least one parameter forconfiguring at least the first mining chip; wherein the input from theuser is a selection of a stored preset value or a manually-enteredvalue.
 10. The non-transitory computer-readable medium of claim 5, thecomputer-executable instructions further causing the computer toperform: automatically switching from a first chip profile configurationof the plurality of chip profile configurations to a second chip profileconfiguration of the plurality of chip profile configurations inresponse to a predetermined condition; wherein the predeterminedcondition is at least one of: a temperature of at least the first miningchip being outside of a predetermined temperature range; a change in theestimated profitability larger than a predetermined profitability changethreshold; and a hashrate of at least the first mining chip beingoutside a predetermined hashrate range.
 11. The non-transitorycomputer-readable medium of claim 10, wherein the automaticallyswitching from the first chip profile configuration to the second chipprofile configuration comprises: incrementally adjusting the chipvoltage by a predetermined incremental voltage value while the miningmachine is continuously mining; and incrementally adjusting the chipfrequency by a predetermined incremental frequency value while themining machine is continuously mining.
 12. The non-transitorycomputer-readable medium of claim 1, wherein the adjusting a chipvoltage supplied to at least the first mining chip comprises: adjustingthe chip voltage while maintaining at least the first mining chip in amining state.
 13. The non-transitory computer-readable medium of claim1, wherein the adjusting a chip frequency of at least the first miningchip comprises: adjusting the chip frequency while maintaining at leastthe first mining chip in a mining state.
 14. The non-transitorycomputer-readable medium of claim 1, wherein the adjusting a chipvoltage supplied to at least the first mining chip and adjusting a chipfrequency of at least the first mining chip to maximize the estimatedprofitability comprises: setting the chip voltage to a maximum chipvoltage value; iteratively performing the following until a hashrate ofthe mining chip falls below a hashrate threshold: measuring a hashrateof at least the first mining chip; comparing the hashrate of at leastthe first mining chip to a hashrate threshold; and reducing the chipvoltage by a predetermined voltage interval value in response to thehashrate being greater than the hashrate threshold; storing a minimumchip voltage at which the hashrate of at least the first mining chip isgreater than the hashrate threshold; iteratively adjusting the chipfrequency to determine a target frequency at which the hashrate of atleast the first mining chip is closest to an ideal hashrate; and storingthe target frequency associated with at least the first mining chip. 15.The non-transitory computer-readable medium of claim 1, wherein: themining machine comprises at least one fan; and the computer-executableinstructions further causing the computer to perform: measuring atemperature of at least the first mining chip or a temperature of afirst hash board of the plurality of hash boards using a temperaturesensor; and decreasing a rotational speed of the at least one fan if thetemperature of the first hash board falls below a predeterminedtemperature.
 16. The non-transitory computer-readable medium of claim 1,wherein: the mining machine comprises at least one fan; and thecomputer-executable instructions further causing the computer toperform: measuring a temperature of at least the first mining chip or atemperature of a first hash board of the plurality of hash boards usinga temperature sensor; and increasing a rotational speed of the at leastone fan if the temperature of the first hash board rises above apredetermined temperature.
 17. A non-transitory computer-readable mediumstoring thereon computer-executable instructions that, when executed bya processor in communication with a mining machine including a pluralityof hash boards each including a plurality of mining chips, cause theprocessor to perform: measuring a temperature of at least first miningchip of the plurality of mining chips or a first hash board of theplurality of hash boards using a temperature sensor; and adjusting achip voltage supplied to at least the first mining chip and adjusting achip frequency of at least the first mining chip to control thetemperature so as to maintain the temperature within a predeterminedtemperature range.
 18. The non-transitory computer-readable medium ofclaim 17, the computer-executable instructions further causing thecomputer to perform: storing a plurality of chip profile configurationsin a memory; wherein each chip profile configuration of the plurality ofchip profile configurations comprises at least one parameter forconfiguring at least the first mining chip.
 19. The non-transitorycomputer-readable medium of claim 18, wherein: the at least oneparameter comprises a voltage range for at least the first mining chipand a frequency range for at least the first mining chip; and adjustinga chip voltage supplied to at least the first mining chip and adjustinga chip frequency of at least the first mining chip comprises at leastone of: dynamically adjusting the chip voltage within the voltage rangeto identify a target chip voltage which is a minimum voltage at which ahashrate of at least the first mining chip is at or above apredetermined hashrate threshold; and dynamically adjusting the chipfrequency within the frequency range to identify a target chip frequencyat which the hashrate of at least the first mining chip is closest to anideal hashrate.
 20. The non-transitory computer-readable medium of claim17, wherein the adjusting a chip voltage supplied to at least the firstmining chip and adjusting a chip frequency of at least the first miningchip comprises adjusting at least one of the chip voltage and the chipfrequency while maintaining at least the first mining chip in a miningstate.
 21. A method for cryptocurrency mining, the method comprising:providing a mining device comprising: a mother board; a power supply inoperable communication with the mother board; an input-output interfacein operable communication with the mother board; and a plurality of hashboards each comprising a plurality of mining chips, the plurality ofhash boards being in operable communication with the mother board;establishing communication with the mining device via an externalnetwork; establishing communication between the mining device and anexternal device via the external network; retrieving at least one profitvariable from the external device via the external network; calculatingan estimated profitability of at least a first mining chip of theplurality of mining chips as a function of at least a hashrate of atleast the first mining chip, a power consumption of at least the firstmining chip, and the at least one profit variable; and adjusting a chipvoltage supplied to at least the first mining chip and adjusting a chipfrequency of at least the first mining chip to maximize the estimatedprofitability; wherein: the power consumption of at least the firstmining chip is calculated based on at least two of the chip voltage, thechip frequency, and the temperature of at least the first mining chip.22. The method of claim 21, further comprising: storing a plurality ofchip profile configurations in a memory; wherein each chip profileconfiguration of the plurality of chip profile configurations comprisesat least one parameter for configuring at least the first mining chip.23. The method of claim 22, further comprising: providing auser-interface in operable communication with the mining device via theexternal network; and entering an input value of the at least oneparameter; wherein the entering the input value of the at least oneparameter comprises at least one of: selecting a stored preset value forthe at least one parameter; and manually entering a value.
 24. Themethod of claim 22, wherein: the at least one parameter comprises avoltage range for at least the first mining chip and a frequency rangefor at least the first mining chip; and adjusting a chip voltagesupplied to at least the first mining chip and adjusting a chipfrequency of at least the first mining chip comprises at least one of:dynamically adjusting the chip voltage within the voltage range toidentify a target chip voltage which is a minimum voltage at which ahashrate of at least the first mining chip is at or above apredetermined hashrate threshold; and dynamically adjusting the chipfrequency within the frequency range to identify a target chip frequencyat which the hashrate of at least the first mining chip is closest to anideal hashrate.
 25. The method of claim 21, wherein the adjusting a chipvoltage supplied to at least the first mining chip and adjusting a chipfrequency of at least the first mining chip comprises: adjusting atleast one of the chip voltage and the chip frequency while maintainingat least the first mining chip in a mining state.
 26. A system forcryptocurrency mining, the system comprising: a mining devicecomprising: a mother board; a power supply in operable communicationwith the mother board; an input-output interface in operablecommunication with the mother board; and a plurality of hash boards eachcomprising a plurality of mining chips, the plurality of hash boardsbeing in operable communication with the mother board; a dynamic tuningfirmware in operable communication with the mother board; wherein: thedynamic tuning firmware is configured to: establish communication withan external device via an external network; retrieve at least one profitvariable from the external device via the external network; calculate anestimated profitability of at least a first mining chip of the pluralityof mining chips as a function of at least a hashrate of at least thefirst mining chip, a power consumption of at least the first miningchip, and the at least one profit variable; and adjust a chip voltagesupplied to at least the first mining chip and adjust a chip frequencyof at least the first mining chip to maximize the estimatedprofitability; and the plurality of mining chips comprises anapplication-specific integrated circuit; and the estimated powerconsumption of at least the first mining chip is calculated based on atleast two of the chip voltage, the chip frequency, and the temperatureof at least the first mining chip.
 27. The system of claim 26, furthercomprising: a temperature sensor configured to measure a temperature ofat least the first mining chip or a temperature of a first hash board ofthe plurality of hash boards, the temperature sensor being in operablecommunication with the mother board; wherein the dynamic tuning firmwareis further configured to adjust the chip voltage and the chip frequencyin response to the temperature being outside of a predeterminedtemperature range.
 28. The system of claim 26, wherein the dynamictuning firmware is configured to adjust the chip voltage and the chipfrequency while at least the first mining chip remains in a miningstate.
 29. The system of claim 26, further comprising: a memory storinga plurality of chip profile configurations; wherein each chip profileconfiguration of the plurality of chip profile configurations comprisesat least one parameter for configuring at least the first mining chip;and the dynamic tuning firmware is further configured to automaticallyswitch from a first chip profile configuration of the plurality of chipprofile configurations to a second chip profile configuration of theplurality of chip profile configurations in response to a predeterminedcondition.
 30. The system for cryptocurrency mining of claim 26, whereinthe dynamic tuning firmware is configured to adjust the chip voltage andadjust the chip frequency when a change in the estimated profitabilityexceeds a predetermined profitability change threshold.